Show of Hands:Votes: Party classification:: Votes_Q_02_cnk
bdanalytics
Date: (Sun) May 29, 2016
Introduction:
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret## Loading required package: lattice## Loading required package: ggplot2source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "R"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>") -> OOB performed worse than "Hhold.fctr"
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012"
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024" # Done
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(raw1) {
raw <- raw1
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
glbFeatsDerive[["Hhold.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)
raw <- gsub("Married (no kids)" , "MKn", raw, fixed = TRUE)
raw <- gsub("Married (w/kids)" , "MKy", raw, fixed = TRUE)
raw <- gsub("Single (no kids)" , "SKn", raw, fixed = TRUE)
raw <- gsub("Single (w/kids)" , "SKy", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("HouseholdStatus"))
glbFeatsDerive[["Edn.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Current K-12" , "K12", raw, fixed = TRUE)
raw <- gsub("High School Diploma" , "HSD", raw, fixed = TRUE)
raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
raw <- gsub("Associate's Degree" , "Ast", raw, fixed = TRUE)
raw <- gsub("Bachelor's Degree" , "Bcr", raw, fixed = TRUE)
raw <- gsub("Master's Degree" , "Msr", raw, fixed = TRUE)
raw <- gsub("Doctoral Degree" , "PhD", raw, fixed = TRUE)
return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
ordered = TRUE))
}
, args = c("EducationLevel"))
# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))
glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
mapfn = function(raw1) {
raw1[raw1 %in% ""] <- "NA"
rawVal <- unique(raw1)
if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
raw1 <- gsub("Idealist" , "Id", raw1, fixed = TRUE)
raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
}
return(relevel(as.factor(raw1), ref = "NA"))
}
, args = c(qsn))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))
#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]]))));
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial)
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet") else
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "9.342e-02")
)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "<mdlId>"),
# glmnetTuneParams))
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
glb_preproc_methods <- NULL
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSelId <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
# require(tidyr)
# obsOutFinDf <- obsOutFinDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutFinDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutFinDf) {
# }
)
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
# txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glbOut <- list(pfx = "Votes_Q_02_cnk_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- "cluster.data" #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #NULL #default: script will save envir at end of this chunk
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#load("Votes_Q_02_cnk_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL,
ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))## label step_major step_minor label_minor bgn end elapsed
## 1 cluster.data 1 0 0 8.05 NA NAStep 1.0: cluster data
chunk option: eval=
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end
## 1 cluster.data 1 0 0 8.050 9.292
## 2 partition.data.training 2 0 0 9.292 NA
## elapsed
## 1 1.242
## 2 NAStep 2.0: partition data training
## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk## Loading required package: reshape2## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 1.08 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 1.08 secs"## Loading required package: sampling##
## Attaching package: 'sampling'## The following object is masked from 'package:caret':
##
## cluster## [1] "lclgetMatrixCorrelation: duration: 40.428000 secs"
## [1] "cor of Fit vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 15.229000 secs"
## [1] "cor of New vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 50.311000 secs"
## [1] "cor of Fit vs. New: 1.0000"## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 108.10 secs"## Party.Democrat Party.Republican Party.NA
## NA NA 1392
## Fit 2357 2091 NA
## OOB 594 526 NA
## Party.Democrat Party.Republican Party.NA
## NA NA 1
## Fit 0.5299011 0.4700989 NA
## OOB 0.5303571 0.4696429 NA
## Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6 SKn 1920 511 638 0.43165468 0.456250000
## 2 MKy 1296 298 371 0.29136691 0.266071429
## 1 MKn 516 136 169 0.11600719 0.121428571
## 3 N 367 83 102 0.08250899 0.074107143
## 7 SKy 147 53 65 0.03304856 0.047321429
## 4 PKn 150 30 37 0.03372302 0.026785714
## 5 PKy 52 9 10 0.01169065 0.008035714
## .freqRatio.Tst
## 6 0.458333333
## 2 0.266522989
## 1 0.121408046
## 3 0.073275862
## 7 0.046695402
## 4 0.026580460
## 5 0.007183908## [1] "glbObsAll: "## [1] 6960 219## [1] "glbObsTrn: "## [1] 5568 219## [1] "glbObsFit: "## [1] 4448 218## [1] "glbObsOOB: "## [1] 1120 218## [1] "glbObsNew: "## [1] 1392 218## [1] "partition.data.training chunk: teardown: elapsed: 109.04 secs"## label step_major step_minor label_minor bgn
## 2 partition.data.training 2 0 0 9.292
## 3 select.features 3 0 0 118.368
## end elapsed
## 2 118.368 109.076
## 3 NA NAStep 3.0: select features
## [1] "cor(Q98059.fctr, Q98078.fctr)=0.7689"
## [1] "cor(Party.fctr, Q98059.fctr)=0.0172"
## [1] "cor(Party.fctr, Q98078.fctr)=0.0257"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98059.fctr as highly correlated with Q98078.fctr## [1] "cor(Q99480.fctr, Q99581.fctr)=0.7660"
## [1] "cor(Party.fctr, Q99480.fctr)=-0.0344"
## [1] "cor(Party.fctr, Q99581.fctr)=-0.0104"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q99581.fctr as highly correlated with Q99480.fctr## [1] "cor(Q108855.fctr, Q108856.fctr)=0.7430"
## [1] "cor(Party.fctr, Q108855.fctr)=-0.0371"
## [1] "cor(Party.fctr, Q108856.fctr)=-0.0140"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108856.fctr as highly correlated with Q108855.fctr## [1] "cor(Q122770.fctr, Q122771.fctr)=0.7379"
## [1] "cor(Party.fctr, Q122770.fctr)=-0.0195"
## [1] "cor(Party.fctr, Q122771.fctr)=-0.0348"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q122770.fctr as highly correlated with Q122771.fctr## [1] "cor(Q106272.fctr, Q106388.fctr)=0.7339"
## [1] "cor(Party.fctr, Q106272.fctr)=-0.0401"
## [1] "cor(Party.fctr, Q106388.fctr)=-0.0342"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q106388.fctr as highly correlated with Q106272.fctr## [1] "cor(Q100680.fctr, Q100689.fctr)=0.7292"
## [1] "cor(Party.fctr, Q100680.fctr)=0.0158"
## [1] "cor(Party.fctr, Q100689.fctr)=0.0257"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100680.fctr as highly correlated with Q100689.fctr## [1] "cor(Q99480.fctr, Q99716.fctr)=0.7252"
## [1] "cor(Party.fctr, Q99480.fctr)=-0.0344"
## [1] "cor(Party.fctr, Q99716.fctr)=0.0209"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q99716.fctr as highly correlated with Q99480.fctr## [1] "cor(Q120472.fctr, Q120650.fctr)=0.7126"
## [1] "cor(Party.fctr, Q120472.fctr)=-0.0462"
## [1] "cor(Party.fctr, Q120650.fctr)=-0.0271"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q120650.fctr as highly correlated with Q120472.fctr## [1] "cor(Q98869.fctr, Q99480.fctr)=0.7084"
## [1] "cor(Party.fctr, Q98869.fctr)=-0.0277"
## [1] "cor(Party.fctr, Q99480.fctr)=-0.0344"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98869.fctr as highly correlated with Q99480.fctr## [1] "cor(Q123464.fctr, Q123621.fctr)=0.7078"
## [1] "cor(Party.fctr, Q123464.fctr)=-0.0136"
## [1] "cor(Party.fctr, Q123621.fctr)=-0.0255"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q123464.fctr as highly correlated with Q123621.fctr## [1] "cor(Q108754.fctr, Q108855.fctr)=0.7005"
## [1] "cor(Party.fctr, Q108754.fctr)=-0.0081"
## [1] "cor(Party.fctr, Q108855.fctr)=-0.0371"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108754.fctr as highly correlated with Q108855.fctr## cor.y exclude.as.feat cor.y.abs cor.high.X
## Q109244.fctr 0.1203812469 0 0.1203812469 <NA>
## Hhold.fctr 0.0511386673 0 0.0511386673 <NA>
## Edn.fctr 0.0359295351 0 0.0359295351 <NA>
## Q101163.fctr 0.0295046473 0 0.0295046473 <NA>
## Q100689.fctr 0.0256915080 0 0.0256915080 <NA>
## Q98078.fctr 0.0256516490 0 0.0256516490 <NA>
## Q99716.fctr 0.0209286674 0 0.0209286674 Q99480.fctr
## Q120379.fctr 0.0206291292 0 0.0206291292 <NA>
## Q121699.fctr 0.0196933075 0 0.0196933075 <NA>
## Q105840.fctr 0.0195569165 0 0.0195569165 <NA>
## Q113583.fctr 0.0191894717 0 0.0191894717 <NA>
## Q115195.fctr 0.0174522586 0 0.0174522586 <NA>
## Q102089.fctr 0.0174087944 0 0.0174087944 <NA>
## Q98059.fctr 0.0171637755 0 0.0171637755 Q98078.fctr
## Q114386.fctr 0.0168013326 0 0.0168013326 <NA>
## Q100680.fctr 0.0157762454 0 0.0157762454 Q100689.fctr
## Q108342.fctr 0.0151842510 0 0.0151842510 <NA>
## Q111848.fctr 0.0141099384 0 0.0141099384 <NA>
## YOB.Age.fctr 0.0129198495 0 0.0129198495 <NA>
## Q118892.fctr 0.0125250379 0 0.0125250379 <NA>
## Q102687.fctr 0.0120079165 0 0.0120079165 <NA>
## Q115390.fctr 0.0119300319 0 0.0119300319 <NA>
## Q119851.fctr 0.0093381833 0 0.0093381833 <NA>
## Q114517.fctr 0.0084741753 0 0.0084741753 <NA>
## Q120012.fctr 0.0084652930 0 0.0084652930 <NA>
## Q109367.fctr 0.0080456026 0 0.0080456026 <NA>
## Q114961.fctr 0.0079206587 0 0.0079206587 <NA>
## Q121700.fctr 0.0067756198 0 0.0067756198 <NA>
## Q124122.fctr 0.0061257448 0 0.0061257448 <NA>
## Q111220.fctr 0.0055758571 0 0.0055758571 <NA>
## Q113992.fctr 0.0041479796 0 0.0041479796 <NA>
## Q121011.fctr 0.0037329030 0 0.0037329030 <NA>
## Q106042.fctr 0.0032327194 0 0.0032327194 <NA>
## Q116448.fctr 0.0031731051 0 0.0031731051 <NA>
## Q116601.fctr 0.0022379241 0 0.0022379241 <NA>
## Q104996.fctr 0.0012202806 0 0.0012202806 <NA>
## Q102906.fctr 0.0011540297 0 0.0011540297 <NA>
## Q113584.fctr 0.0011387024 0 0.0011387024 <NA>
## Q108950.fctr 0.0010567028 0 0.0010567028 <NA>
## Q102674.fctr 0.0009759844 0 0.0009759844 <NA>
## Q103293.fctr 0.0005915534 0 0.0005915534 <NA>
## Q112478.fctr 0.0001517248 0 0.0001517248 <NA>
## Q114748.fctr -0.0008477228 0 0.0008477228 <NA>
## Q107491.fctr -0.0014031814 0 0.0014031814 <NA>
## Q100562.fctr -0.0017132769 0 0.0017132769 <NA>
## Q108617.fctr -0.0024119725 0 0.0024119725 <NA>
## Q100010.fctr -0.0024291540 0 0.0024291540 <NA>
## Q115602.fctr -0.0027844465 0 0.0027844465 <NA>
## Q116953.fctr -0.0029786716 0 0.0029786716 <NA>
## Q115610.fctr -0.0035255582 0 0.0035255582 <NA>
## Q106997.fctr -0.0041749086 0 0.0041749086 <NA>
## Q120978.fctr -0.0044187616 0 0.0044187616 <NA>
## Q112512.fctr -0.0056768212 0 0.0056768212 <NA>
## Q108343.fctr -0.0060665340 0 0.0060665340 <NA>
## Q96024.fctr -0.0069116541 0 0.0069116541 <NA>
## Q106389.fctr -0.0077498918 0 0.0077498918 <NA>
## .rnorm -0.0078039520 0 0.0078039520 <NA>
## Q108754.fctr -0.0080847764 0 0.0080847764 Q108855.fctr
## Q98578.fctr -0.0081164509 0 0.0081164509 <NA>
## Q101162.fctr -0.0099412952 0 0.0099412952 <NA>
## Q115777.fctr -0.0101315203 0 0.0101315203 <NA>
## Q99581.fctr -0.0103662478 0 0.0103662478 Q99480.fctr
## Q124742.fctr -0.0111642906 0 0.0111642906 <NA>
## Q116797.fctr -0.0112749656 0 0.0112749656 <NA>
## Q112270.fctr -0.0116157798 0 0.0116157798 <NA>
## YOB -0.0116828198 1 0.0116828198 <NA>
## Q118237.fctr -0.0117079669 0 0.0117079669 <NA>
## Q119650.fctr -0.0125645475 0 0.0125645475 <NA>
## Q111580.fctr -0.0132382335 0 0.0132382335 <NA>
## Q123464.fctr -0.0136140083 0 0.0136140083 Q123621.fctr
## Q117193.fctr -0.0138241599 0 0.0138241599 <NA>
## Q99982.fctr -0.0139727928 0 0.0139727928 <NA>
## Q108856.fctr -0.0140363785 0 0.0140363785 Q108855.fctr
## Q118233.fctr -0.0147269325 0 0.0147269325 <NA>
## Q102289.fctr -0.0155850393 0 0.0155850393 <NA>
## Q116197.fctr -0.0158561766 0 0.0158561766 <NA>
## Income.fctr -0.0159635458 0 0.0159635458 <NA>
## Q118232.fctr -0.0171321152 0 0.0171321152 <NA>
## Q120194.fctr -0.0172986920 0 0.0172986920 <NA>
## Q114152.fctr -0.0175013163 0 0.0175013163 <NA>
## Q122770.fctr -0.0194639697 0 0.0194639697 Q122771.fctr
## Q117186.fctr -0.0198853672 0 0.0198853672 <NA>
## Q105655.fctr -0.0198994078 0 0.0198994078 <NA>
## Q106993.fctr -0.0207428635 0 0.0207428635 <NA>
## Q119334.fctr -0.0226894034 0 0.0226894034 <NA>
## Q122120.fctr -0.0229287700 0 0.0229287700 <NA>
## Q116441.fctr -0.0237358205 0 0.0237358205 <NA>
## Q118117.fctr -0.0253544150 0 0.0253544150 <NA>
## Q123621.fctr -0.0255329743 0 0.0255329743 <NA>
## Q122769.fctr -0.0259739146 0 0.0259739146 <NA>
## Q120650.fctr -0.0270889067 0 0.0270889067 Q120472.fctr
## Q98869.fctr -0.0276734114 0 0.0276734114 Q99480.fctr
## .pos -0.0302037138 1 0.0302037138 <NA>
## USER_ID -0.0302304868 1 0.0302304868 <NA>
## Q107869.fctr -0.0304661021 0 0.0304661021 <NA>
## Q120014.fctr -0.0318620439 0 0.0318620439 <NA>
## Q115899.fctr -0.0324177950 0 0.0324177950 <NA>
## Q106388.fctr -0.0341579350 0 0.0341579350 Q106272.fctr
## Q99480.fctr -0.0344412239 0 0.0344412239 <NA>
## Q122771.fctr -0.0348421015 0 0.0348421015 <NA>
## Q108855.fctr -0.0370970211 0 0.0370970211 <NA>
## Q110740.fctr -0.0380691243 0 0.0380691243 <NA>
## Q106272.fctr -0.0400926462 0 0.0400926462 <NA>
## Q101596.fctr -0.0409784077 0 0.0409784077 <NA>
## Q116881.fctr -0.0416860293 0 0.0416860293 <NA>
## Q120472.fctr -0.0462030674 0 0.0462030674 <NA>
## Q98197.fctr -0.0549342527 0 0.0549342527 <NA>
## Q113181.fctr -0.0808753072 0 0.0808753072 <NA>
## Q115611.fctr -0.0904468203 0 0.0904468203 <NA>
## Gender.fctr -0.1027400851 0 0.1027400851 <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## Q109244.fctr 1.125916 0.05387931 FALSE FALSE FALSE
## Hhold.fctr 1.525094 0.12571839 FALSE FALSE FALSE
## Edn.fctr 1.392610 0.14367816 FALSE FALSE FALSE
## Q101163.fctr 1.327394 0.05387931 FALSE FALSE FALSE
## Q100689.fctr 1.029800 0.05387931 FALSE FALSE FALSE
## Q98078.fctr 1.266595 0.05387931 FALSE FALSE FALSE
## Q99716.fctr 1.328693 0.05387931 FALSE FALSE FALSE
## Q120379.fctr 1.046326 0.05387931 FALSE FALSE FALSE
## Q121699.fctr 1.507127 0.05387931 FALSE FALSE FALSE
## Q105840.fctr 1.275362 0.05387931 FALSE FALSE FALSE
## Q113583.fctr 1.102515 0.05387931 FALSE FALSE FALSE
## Q115195.fctr 1.065496 0.05387931 FALSE FALSE FALSE
## Q102089.fctr 1.055963 0.05387931 FALSE FALSE FALSE
## Q98059.fctr 1.493810 0.05387931 FALSE FALSE FALSE
## Q114386.fctr 1.092072 0.05387931 FALSE FALSE FALSE
## Q100680.fctr 1.102386 0.05387931 FALSE FALSE FALSE
## Q108342.fctr 1.048292 0.05387931 FALSE FALSE FALSE
## Q111848.fctr 1.113602 0.05387931 FALSE FALSE FALSE
## YOB.Age.fctr 1.005794 0.16163793 FALSE FALSE FALSE
## Q118892.fctr 1.347380 0.05387931 FALSE FALSE FALSE
## Q102687.fctr 1.256545 0.05387931 FALSE FALSE FALSE
## Q115390.fctr 1.150505 0.05387931 FALSE FALSE FALSE
## Q119851.fctr 1.244519 0.05387931 FALSE FALSE FALSE
## Q114517.fctr 1.183374 0.05387931 FALSE FALSE FALSE
## Q120012.fctr 1.047185 0.05387931 FALSE FALSE FALSE
## Q109367.fctr 1.008571 0.05387931 FALSE FALSE FALSE
## Q114961.fctr 1.250436 0.05387931 FALSE FALSE FALSE
## Q121700.fctr 1.708221 0.05387931 FALSE FALSE TRUE
## Q124122.fctr 1.412807 0.05387931 FALSE FALSE TRUE
## Q111220.fctr 1.262849 0.05387931 FALSE FALSE TRUE
## Q113992.fctr 1.267442 0.05387931 FALSE FALSE TRUE
## Q121011.fctr 1.153676 0.05387931 FALSE FALSE TRUE
## Q106042.fctr 1.247738 0.05387931 FALSE FALSE TRUE
## Q116448.fctr 1.161031 0.05387931 FALSE FALSE TRUE
## Q116601.fctr 1.394914 0.05387931 FALSE FALSE TRUE
## Q104996.fctr 1.173840 0.05387931 FALSE FALSE TRUE
## Q102906.fctr 1.053396 0.05387931 FALSE FALSE TRUE
## Q113584.fctr 1.212486 0.05387931 FALSE FALSE TRUE
## Q108950.fctr 1.103872 0.05387931 FALSE FALSE TRUE
## Q102674.fctr 1.073412 0.05387931 FALSE FALSE TRUE
## Q103293.fctr 1.122287 0.05387931 FALSE FALSE TRUE
## Q112478.fctr 1.113648 0.05387931 FALSE FALSE TRUE
## Q114748.fctr 1.051125 0.05387931 FALSE FALSE TRUE
## Q107491.fctr 1.419021 0.05387931 FALSE FALSE TRUE
## Q100562.fctr 1.217215 0.05387931 FALSE FALSE TRUE
## Q108617.fctr 1.390618 0.05387931 FALSE FALSE TRUE
## Q100010.fctr 1.268156 0.05387931 FALSE FALSE TRUE
## Q115602.fctr 1.322302 0.05387931 FALSE FALSE TRUE
## Q116953.fctr 1.039180 0.05387931 FALSE FALSE TRUE
## Q115610.fctr 1.359695 0.05387931 FALSE FALSE TRUE
## Q106997.fctr 1.177632 0.05387931 FALSE FALSE TRUE
## Q120978.fctr 1.131963 0.05387931 FALSE FALSE TRUE
## Q112512.fctr 1.299253 0.05387931 FALSE FALSE TRUE
## Q108343.fctr 1.064910 0.05387931 FALSE FALSE TRUE
## Q96024.fctr 1.144428 0.05387931 FALSE FALSE TRUE
## Q106389.fctr 1.341307 0.05387931 FALSE FALSE TRUE
## .rnorm 1.000000 100.00000000 FALSE FALSE FALSE
## Q108754.fctr 1.008090 0.05387931 FALSE FALSE FALSE
## Q98578.fctr 1.093556 0.05387931 FALSE FALSE FALSE
## Q101162.fctr 1.103229 0.05387931 FALSE FALSE FALSE
## Q115777.fctr 1.140288 0.05387931 FALSE FALSE FALSE
## Q99581.fctr 1.375000 0.05387931 FALSE FALSE FALSE
## Q124742.fctr 2.565379 0.05387931 FALSE FALSE FALSE
## Q116797.fctr 1.009589 0.05387931 FALSE FALSE FALSE
## Q112270.fctr 1.254284 0.05387931 FALSE FALSE FALSE
## YOB 1.027559 1.41882184 FALSE FALSE FALSE
## Q118237.fctr 1.088017 0.05387931 FALSE FALSE FALSE
## Q119650.fctr 1.456978 0.05387931 FALSE FALSE FALSE
## Q111580.fctr 1.024977 0.05387931 FALSE FALSE FALSE
## Q123464.fctr 1.326681 0.05387931 FALSE FALSE FALSE
## Q117193.fctr 1.140665 0.05387931 FALSE FALSE FALSE
## Q99982.fctr 1.339380 0.05387931 FALSE FALSE FALSE
## Q108856.fctr 1.080645 0.05387931 FALSE FALSE FALSE
## Q118233.fctr 1.199142 0.05387931 FALSE FALSE FALSE
## Q102289.fctr 1.033482 0.05387931 FALSE FALSE FALSE
## Q116197.fctr 1.073778 0.05387931 FALSE FALSE FALSE
## Income.fctr 1.256724 0.12571839 FALSE FALSE FALSE
## Q118232.fctr 1.365812 0.05387931 FALSE FALSE FALSE
## Q120194.fctr 1.016716 0.05387931 FALSE FALSE FALSE
## Q114152.fctr 1.027617 0.05387931 FALSE FALSE FALSE
## Q122770.fctr 1.008802 0.05387931 FALSE FALSE FALSE
## Q117186.fctr 1.053878 0.05387931 FALSE FALSE FALSE
## Q105655.fctr 1.079316 0.05387931 FALSE FALSE FALSE
## Q106993.fctr 1.327392 0.05387931 FALSE FALSE FALSE
## Q119334.fctr 1.081498 0.05387931 FALSE FALSE FALSE
## Q122120.fctr 1.297443 0.05387931 FALSE FALSE FALSE
## Q116441.fctr 1.019645 0.05387931 FALSE FALSE FALSE
## Q118117.fctr 1.174006 0.05387931 FALSE FALSE FALSE
## Q123621.fctr 1.466381 0.05387931 FALSE FALSE FALSE
## Q122769.fctr 1.060606 0.05387931 FALSE FALSE FALSE
## Q120650.fctr 1.896247 0.05387931 FALSE FALSE FALSE
## Q98869.fctr 1.080860 0.05387931 FALSE FALSE FALSE
## .pos 1.000000 100.00000000 FALSE FALSE FALSE
## USER_ID 1.000000 100.00000000 FALSE FALSE FALSE
## Q107869.fctr 1.211050 0.05387931 FALSE FALSE FALSE
## Q120014.fctr 1.044944 0.05387931 FALSE FALSE FALSE
## Q115899.fctr 1.197849 0.05387931 FALSE FALSE FALSE
## Q106388.fctr 1.065033 0.05387931 FALSE FALSE FALSE
## Q99480.fctr 1.225404 0.05387931 FALSE FALSE FALSE
## Q122771.fctr 1.414753 0.05387931 FALSE FALSE FALSE
## Q108855.fctr 1.273980 0.05387931 FALSE FALSE FALSE
## Q110740.fctr 1.050779 0.05387931 FALSE FALSE FALSE
## Q106272.fctr 1.116536 0.05387931 FALSE FALSE FALSE
## Q101596.fctr 1.041667 0.05387931 FALSE FALSE FALSE
## Q116881.fctr 1.010066 0.05387931 FALSE FALSE FALSE
## Q120472.fctr 1.292633 0.05387931 FALSE FALSE FALSE
## Q98197.fctr 1.129371 0.05387931 FALSE FALSE FALSE
## Q113181.fctr 1.006354 0.05387931 FALSE FALSE FALSE
## Q115611.fctr 1.194859 0.05387931 FALSE FALSE FALSE
## Gender.fctr 1.561033 0.05387931 FALSE FALSE FALSE## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## [1] cor.y exclude.as.feat cor.y.abs cor.high.X
## [5] freqRatio percentUnique zeroVar nzv
## [9] is.cor.y.abs.low
## <0 rows> (or 0-length row.names)## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024 .lcn
## 2836 2858 1392## [1] "glb_feats_df:"## [1] 110 12## id exclude.as.feat rsp_var
## Party.fctr Party.fctr TRUE TRUE## id cor.y exclude.as.feat cor.y.abs cor.high.X
## USER_ID USER_ID -0.03023049 TRUE 0.03023049 <NA>
## Party.fctr Party.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## USER_ID 1 100 FALSE FALSE FALSE
## Party.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID NA NA FALSE TRUE
## Party.fctr NA NA NA NA
## rsp_var
## USER_ID NA
## Party.fctr TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end
## 3 select.features 3 0 0 118.368 124.554
## 4 fit.models 4 0 0 124.554 NA
## elapsed
## 3 6.186
## 4 NAStep 4.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 125.075 NA NA# load(paste0(glbOut$pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
get_dsp_models_df <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor bgn end
## 1 fit.models_0_bgn 1 0 setup 125.075 125.108
## 2 fit.models_0_MFO 1 1 myMFO_classfr 125.108 NA
## elapsed
## 1 0.033
## 2 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: MFO###myMFO_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.441000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] R D
## Levels: R D
## [1] "unique.prob:"
## y
## D R
## 0.5299011 0.4700989
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.836000 secs"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "myfit_mdl: train diagnostics complete: 0.838000 secs"## Loading required namespace: pROC## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## R D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989
## Prediction
## Reference R D
## R 2091 0
## D 2357 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4700989 0.0000000 0.4553427 0.4848945 0.5299011
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## R D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989
## Prediction
## Reference R D
## R 526 0
## D 594 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.696429e-01 0.000000e+00 4.400805e-01 4.993651e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.999790e-01 9.194240e-131
## [1] "myfit_mdl: predict complete: 5.670000 secs"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm 0 0.386
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.003 0.5 0 1
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0.6395473 0.4700989
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4553427 0.4848945 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 0 1 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6391252 0.4696429
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4400805 0.4993651 0
## [1] "myfit_mdl: exit: 5.679000 secs"
## label step_major step_minor label_minor bgn
## 2 fit.models_0_MFO 1 1 myMFO_classfr 125.108
## 3 fit.models_0_Random 1 2 myrandom_classfr 130.793
## end elapsed
## 2 130.793 5.685
## 3 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Random###myrandom_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.432000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.709000 secs"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "myfit_mdl: train diagnostics complete: 0.710000 secs"
## [1] "in Random.Classifier$prob"
## Prediction
## Reference R D
## R 2091 0
## D 2357 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4700989 0.0000000 0.4553427 0.4848945 0.5299011
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] "in Random.Classifier$prob"
## Prediction
## Reference R D
## R 526 0
## D 594 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.696429e-01 0.000000e+00 4.400805e-01 4.993651e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.999790e-01 9.194240e-131
## [1] "myfit_mdl: predict complete: 6.510000 secs"
## id feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.27 0.002 0.4942483
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.4619799 0.5265168 0.5073101 0.55
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6395473 0.4700989 0.4553427
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.4848945 0 0.523569 0.5
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.547138 0.5191202 0.55 0.6391252
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.4696429 0.4400805 0.4993651
## max.Kappa.OOB
## 1 0
## [1] "myfit_mdl: exit: 6.523000 secs"# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## bgn end elapsed
## 3 130.793 137.327 6.534
## 4 137.327 NA NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.700000 secs"## Loading required package: glmnet## Loading required package: Matrix## Loaded glmnet 2.0-5## Fitting alpha = 0.1, lambda = 0.00248 on full training set
## [1] "myfit_mdl: train complete: 1.500000 secs"
## Length Class Mode
## a0 58 -none- numeric
## beta 232 dgCMatrix S4
## df 58 -none- numeric
## dim 2 -none- numeric
## lambda 58 -none- numeric
## dev.ratio 58 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 4 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrM Q109244.fctrNo Q109244.fctrYes
## 0.2665753 -0.2101506 -0.4308362 1.2139586
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "Gender.fctrF" "Gender.fctrM" "Q109244.fctrNo"
## [5] "Q109244.fctrYes"
## [1] "myfit_mdl: train diagnostics complete: 1.596000 secs"
## Prediction
## Reference R D
## R 1950 141
## D 1762 595
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.721673e-01 1.772539e-01 5.574714e-01 5.867683e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 8.241814e-09 7.365212e-302
## Prediction
## Reference R D
## R 484 42
## D 447 147
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.633929e-01 1.605510e-01 5.337655e-01 5.926864e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 1.432605e-02 1.447405e-74
## [1] "myfit_mdl: predict complete: 5.874000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.79 0.062 0.5971118
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5480631 0.6461604 0.3580613 0.6
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6720662 0.5721673 0.5574714
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5867683 0.1772539 0.5896897 0.5228137
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.6565657 0.3658672 0.6 0.6643789
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5633929 0.5337655 0.5926864
## max.Kappa.OOB
## 1 0.160551
## [1] "myfit_mdl: exit: 5.887000 secs"if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.686000 secs"## Loading required package: rpart## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0225 on full training set
## [1] "myfit_mdl: train complete: 2.260000 secs"## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4448
##
## CP nsplit rel error
## 1 0.08990913 0 1.0000000
## 2 0.05930177 1 0.9100909
## 3 0.02247728 2 0.8507891
##
## Variable importance
## Q109244.fctrYes Q109244.fctrNo Gender.fctrM Gender.fctrF
## 83 15 1 1
##
## Node number 1: 4448 observations, complexity param=0.08990913
## predicted class=D expected loss=0.4700989 P(node) =1
## class counts: 2091 2357
## probabilities: 0.470 0.530
## left son=2 (3712 obs) right son=3 (736 obs)
## Primary splits:
## Q109244.fctrYes < 0.5 to the left, improve=136.83150, (0 missing)
## Q109244.fctrNo < 0.5 to the right, improve= 84.31128, (0 missing)
## Gender.fctrM < 0.5 to the right, improve= 24.39999, (0 missing)
## Gender.fctrF < 0.5 to the left, improve= 22.65952, (0 missing)
##
## Node number 2: 3712 observations, complexity param=0.05930177
## predicted class=R expected loss=0.4746767 P(node) =0.8345324
## class counts: 1950 1762
## probabilities: 0.525 0.475
## left son=4 (1980 obs) right son=5 (1732 obs)
## Primary splits:
## Q109244.fctrNo < 0.5 to the right, improve=24.259840, (0 missing)
## Gender.fctrM < 0.5 to the right, improve=10.189980, (0 missing)
## Gender.fctrF < 0.5 to the left, improve= 8.193561, (0 missing)
## Surrogate splits:
## Gender.fctrM < 0.5 to the right, agree=0.571, adj=0.080, (0 split)
## Gender.fctrF < 0.5 to the left, agree=0.563, adj=0.063, (0 split)
##
## Node number 3: 736 observations
## predicted class=D expected loss=0.1915761 P(node) =0.1654676
## class counts: 141 595
## probabilities: 0.192 0.808
##
## Node number 4: 1980 observations
## predicted class=R expected loss=0.4212121 P(node) =0.4451439
## class counts: 1146 834
## probabilities: 0.579 0.421
##
## Node number 5: 1732 observations
## predicted class=D expected loss=0.4642032 P(node) =0.3893885
## class counts: 804 928
## probabilities: 0.464 0.536
##
## n= 4448
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4448 2091 D (0.4700989 0.5299011)
## 2) Q109244.fctrYes< 0.5 3712 1762 R (0.5253233 0.4746767)
## 4) Q109244.fctrNo>=0.5 1980 834 R (0.5787879 0.4212121) *
## 5) Q109244.fctrNo< 0.5 1732 804 D (0.4642032 0.5357968) *
## 3) Q109244.fctrYes>=0.5 736 141 D (0.1915761 0.8084239) *
## [1] "myfit_mdl: train diagnostics complete: 3.074000 secs"
## Prediction
## Reference R D
## R 1950 141
## D 1762 595
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.721673e-01 1.772539e-01 5.574714e-01 5.867683e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 8.241814e-09 7.365212e-302
## Prediction
## Reference R D
## R 484 42
## D 447 147
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.633929e-01 1.605510e-01 5.337655e-01 5.926864e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 1.432605e-02 1.447405e-74
## [1] "myfit_mdl: predict complete: 7.695000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.566 0.019 0.5971118
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5480631 0.6461604 0.3676308 0.55
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6720662 0.600045 0.5574714
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5867683 0.1947896 0.5896897 0.5228137
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.6565657 0.3774772 0.55 0.6643789
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.5633929 0.5337655 0.5926864
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.160551 0.0124035 0.02559319
## [1] "myfit_mdl: exit: 7.710000 secs"if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
} ## label step_major step_minor label_minor
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## bgn end elapsed
## 4 137.327 150.965 13.638
## 5 150.965 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] " indepVar: Q109244.fctr,Gender.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q98078.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr"
## [1] "myfit_mdl: setup complete: 0.704000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00248 on full training set
## [1] "myfit_mdl: train complete: 6.005000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 69 -none- numeric
## beta 3588 dgCMatrix S4
## df 69 -none- numeric
## dim 2 -none- numeric
## lambda 69 -none- numeric
## dev.ratio 69 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 52 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Gender.fctrM
## 0.214462392 -0.145896015
## Q109244.fctrNo Q109244.fctrYes
## -0.216275985 0.903504361
## Q109244.fctrNA:Q100689.fctrNo Q109244.fctrYes:Q100689.fctrNo
## 0.208349081 -0.004870424
## Q109244.fctrNA:Q100689.fctrYes Q109244.fctrNo:Q100689.fctrYes
## 0.397412088 0.048879968
## Q109244.fctrYes:Q100689.fctrYes Q109244.fctrNA:Q106272.fctrNo
## 0.262128025 0.066457216
## Q109244.fctrNo:Q106272.fctrNo Q109244.fctrYes:Q106272.fctrNo
## 0.074008287 -0.069508937
## Q109244.fctrNA:Q106272.fctrYes Q109244.fctrNo:Q106272.fctrYes
## -0.125437842 -0.155157463
## Q109244.fctrYes:Q106272.fctrYes Q109244.fctrNA:Q108855.fctrUmm...
## 0.019996659 -0.325540110
## Q109244.fctrNo:Q108855.fctrUmm... Q109244.fctrYes:Q108855.fctrUmm...
## 0.065090912 0.025966636
## Q109244.fctrNo:Q108855.fctrYes! Q109244.fctrNA:Q120472.fctrArt
## -0.166309419 0.045175676
## Q109244.fctrYes:Q120472.fctrArt Q109244.fctrNA:Q120472.fctrScience
## 0.050613747 -0.081869305
## Q109244.fctrNo:Q120472.fctrScience Q109244.fctrNA:Q122771.fctrPc
## -0.045206278 0.026381445
## Q109244.fctrNo:Q122771.fctrPc Q109244.fctrNA:Q122771.fctrPt
## -0.079571797 -0.056075353
## Q109244.fctrNo:Q122771.fctrPt Q109244.fctrYes:Q122771.fctrPt
## -0.312370610 -0.184990100
## Q109244.fctrNA:Q123621.fctrNo Q109244.fctrYes:Q123621.fctrNo
## -0.055648138 0.250541256
## Q109244.fctrNo:Q123621.fctrYes Q109244.fctrYes:Q123621.fctrYes
## -0.118706388 0.234767924
## Q109244.fctrNA:Q98078.fctrNo Q109244.fctrNo:Q98078.fctrNo
## 0.041213383 0.052285528
## Q109244.fctrNA:Q98078.fctrYes Q109244.fctrYes:Q98078.fctrYes
## 0.124665272 0.101993831
## Q109244.fctrNA:Q99480.fctrNo Q109244.fctrNo:Q99480.fctrNo
## 0.285510277 0.345084748
## Q109244.fctrYes:Q99480.fctrNo Q109244.fctrNA:Q99480.fctrYes
## 0.054445838 -0.272288871
## [1] "max lambda < lambdaOpt:"
## (Intercept) Gender.fctrM
## 0.215265192 -0.146631129
## Q109244.fctrNo Q109244.fctrYes
## -0.232130198 0.919519640
## Q109244.fctrNA:Q100689.fctrNo Q109244.fctrYes:Q100689.fctrNo
## 0.218265269 -0.019284614
## Q109244.fctrNA:Q100689.fctrYes Q109244.fctrNo:Q100689.fctrYes
## 0.407359039 0.052982570
## Q109244.fctrYes:Q100689.fctrYes Q109244.fctrNA:Q106272.fctrNo
## 0.257862986 0.066427041
## Q109244.fctrNo:Q106272.fctrNo Q109244.fctrYes:Q106272.fctrNo
## 0.075834582 -0.093768371
## Q109244.fctrNA:Q106272.fctrYes Q109244.fctrNo:Q106272.fctrYes
## -0.132132143 -0.157097270
## Q109244.fctrYes:Q106272.fctrYes Q109244.fctrNA:Q108855.fctrUmm...
## 0.006954145 -0.334091989
## Q109244.fctrNo:Q108855.fctrUmm... Q109244.fctrYes:Q108855.fctrUmm...
## 0.073183683 0.029348178
## Q109244.fctrNo:Q108855.fctrYes! Q109244.fctrNA:Q120472.fctrArt
## -0.161489197 0.047013857
## Q109244.fctrYes:Q120472.fctrArt Q109244.fctrNA:Q120472.fctrScience
## 0.053722253 -0.085734575
## Q109244.fctrNo:Q120472.fctrScience Q109244.fctrNA:Q122771.fctrPc
## -0.046328701 0.031858306
## Q109244.fctrNo:Q122771.fctrPc Q109244.fctrNA:Q122771.fctrPt
## -0.086494530 -0.058868145
## Q109244.fctrNo:Q122771.fctrPt Q109244.fctrYes:Q122771.fctrPt
## -0.320607007 -0.200384753
## Q109244.fctrNA:Q123621.fctrNo Q109244.fctrYes:Q123621.fctrNo
## -0.062593721 0.268705396
## Q109244.fctrNo:Q123621.fctrYes Q109244.fctrYes:Q123621.fctrYes
## -0.120160110 0.251231071
## Q109244.fctrNA:Q98078.fctrNo Q109244.fctrNo:Q98078.fctrNo
## 0.050653191 0.069505105
## Q109244.fctrNA:Q98078.fctrYes Q109244.fctrNo:Q98078.fctrYes
## 0.134149587 0.017836961
## Q109244.fctrYes:Q98078.fctrYes Q109244.fctrNA:Q99480.fctrNo
## 0.109598128 0.281737023
## Q109244.fctrNo:Q99480.fctrNo Q109244.fctrYes:Q99480.fctrNo
## 0.348068744 0.053827527
## Q109244.fctrNA:Q99480.fctrYes Q109244.fctrYes:Q99480.fctrYes
## -0.284681230 -0.012138329
## [1] "myfit_mdl: train diagnostics complete: 6.650000 secs"
## Prediction
## Reference R D
## R 1929 162
## D 1715 642
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.780126e-01 1.870655e-01 5.633390e-01 5.925837e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 6.342747e-11 4.877355e-281
## Prediction
## Reference R D
## R 481 45
## D 433 161
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.732143e-01 1.779779e-01 5.436402e-01 6.024028e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 2.187684e-03 4.121616e-70
## [1] "myfit_mdl: predict complete: 12.210000 secs"
## id
## 1 Interact.High.cor.Y##rcv#glmnet
## feats
## 1 Q109244.fctr,Gender.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q98078.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 5.276 0.355
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6184781 0.5958871 0.6410692 0.3319465
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.65 0.6727114 0.6058167
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.563339 0.5925837 0.2088694
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6031353 0.5665399 0.6397306 0.3571392
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.65 0.6680556 0.5732143
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5436402 0.6024028 0.1779779
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0131213 0.02732571
## [1] "myfit_mdl: exit: 12.224000 secs"# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## 6 fit.models_0_Low.cor.X 1 5 glmnet
## bgn end elapsed
## 5 150.965 163.219 12.254
## 6 163.220 NA NAindepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.697000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0534 on full training set
## [1] "myfit_mdl: train complete: 24.124000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 88 -none- numeric
## beta 20416 dgCMatrix S4
## df 88 -none- numeric
## dim 2 -none- numeric
## lambda 88 -none- numeric
## dev.ratio 88 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 232 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr^4
## 0.1920840842 -0.1005414034
## Edn.fctr^6 Edn.fctr^7
## 0.0334860119 0.0710425863
## Gender.fctrM Hhold.fctrMKy
## -0.0794064511 -0.1371778566
## Hhold.fctrPKn Hhold.fctrSKn
## 0.4997234624 0.0176790778
## Hhold.fctrSKy Income.fctr.Q
## 0.1011680811 -0.0816169876
## Income.fctr.C Income.fctr^4
## -0.1216519159 -0.0166494558
## Q100010.fctrNo Q100680.fctrYes
## 0.0094368536 0.0029292851
## Q100689.fctrYes Q101162.fctrPessimist
## 0.0884714668 -0.0071088785
## Q101163.fctrDad Q101163.fctrMom
## -0.1073473633 0.0759596110
## Q101596.fctrNo Q102687.fctrYes
## -0.0036990765 0.0309988067
## Q104996.fctrNo Q104996.fctrYes
## -0.0444993808 0.0096111358
## Q105655.fctrYes Q105840.fctrNo
## -0.0403102885 -0.0039386021
## Q106042.fctrNo Q106272.fctrYes
## -0.0351407304 -0.0370732762
## Q106389.fctrNo Q106997.fctrGr
## -0.0642024577 -0.0433734852
## Q106997.fctrYy Q107491.fctrYes
## 0.0814150590 0.0165630338
## Q108342.fctrOnline Q108855.fctrYes!
## 0.0631187924 -0.0426051502
## Q108950.fctrRisk-friendly Q109244.fctrNo
## 0.0371748796 -0.3676230271
## Q109244.fctrYes Q110740.fctrMac
## 0.7526090401 0.0158364523
## Q110740.fctrPC Q111220.fctrYes
## -0.0902880024 0.0957826931
## Q111848.fctrYes Q112478.fctrNo
## 0.0250589583 -0.0652435089
## Q113181.fctrNo Q113181.fctrYes
## 0.0925300327 -0.1002574917
## Q113992.fctrYes Q115390.fctrNo
## 0.0125173544 -0.0801709946
## Q115390.fctrYes Q115611.fctrNo
## 0.0226179572 0.1209408406
## Q115611.fctrYes Q115899.fctrCs
## -0.3209136716 0.0774284066
## Q115899.fctrMe Q116197.fctrA.M.
## -0.0123327062 -0.0248274337
## Q116881.fctrHappy Q116881.fctrRight
## 0.0742138891 -0.1383231722
## Q116953.fctrNo Q116953.fctrYes
## -0.0348182328 0.0562055276
## Q117186.fctrHot headed Q118232.fctrId
## -0.0145383547 0.1136211170
## Q118233.fctrNo Q118233.fctrYes
## -0.0170028152 0.0113463031
## Q119650.fctrGiving Q119851.fctrNo
## -0.0007454134 -0.1103781004
## Q119851.fctrYes Q120012.fctrYes
## 0.0120497740 0.0366728192
## Q120014.fctrNo Q120014.fctrYes
## 0.0280032695 -0.0299491592
## Q120194.fctrStudy first Q120379.fctrNo
## 0.0598036091 -0.0497973859
## Q120379.fctrYes Q120472.fctrScience
## 0.1111013195 -0.0359739528
## Q120650.fctrYes Q121699.fctrNo
## -0.0249600547 -0.0544691763
## Q121699.fctrYes Q121700.fctrNo
## 0.0374055344 -0.0072164530
## Q121700.fctrYes Q122120.fctrYes
## 0.0163285151 -0.0198345229
## Q122771.fctrPt Q123464.fctrNo
## -0.1085074600 -0.0188472361
## Q124122.fctrNo Q124122.fctrYes
## -0.0315353659 0.0006062715
## Q124742.fctrNo Q96024.fctrNo
## 0.0312573363 0.0189226203
## Q98059.fctrOnly-child Q98059.fctrYes
## -0.0023811587 0.0653704563
## Q98197.fctrNo Q98197.fctrYes
## 0.1793569818 -0.0826453929
## Q98578.fctrNo Q98869.fctrNo
## -0.0365491345 0.2587862534
## Q99480.fctrNo Q99480.fctrYes
## 0.1316394499 -0.0381190876
## YOB.Age.fctr.L YOB.Age.fctr.Q
## 0.1159818986 0.0236491601
## YOB.Age.fctr^4 YOB.Age.fctr^6
## 0.0449093485 0.0053748587
## YOB.Age.fctr^7 YOB.Age.fctr^8
## -0.0412125185 -0.0632525584
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr^4
## 0.190242606 -0.113726050
## Edn.fctr^6 Edn.fctr^7
## 0.039985577 0.076765149
## Gender.fctrM Hhold.fctrMKy
## -0.079499199 -0.139799243
## Hhold.fctrPKn Hhold.fctrSKn
## 0.516895044 0.023441414
## Hhold.fctrSKy Income.fctr.Q
## 0.112262855 -0.086764305
## Income.fctr.C Income.fctr^4
## -0.131008068 -0.022207230
## Income.fctr^6 Q100010.fctrNo
## 0.005184510 0.015575737
## Q100680.fctrYes Q100689.fctrYes
## 0.005695513 0.096910592
## Q101162.fctrPessimist Q101163.fctrDad
## -0.009462485 -0.112012131
## Q101163.fctrMom Q101596.fctrNo
## 0.077204753 -0.011035252
## Q102687.fctrYes Q103293.fctrNo
## 0.036506262 -0.003992889
## Q104996.fctrNo Q104996.fctrYes
## -0.046964218 0.013628231
## Q105655.fctrYes Q105840.fctrNo
## -0.045689342 -0.004398376
## Q106042.fctrNo Q106272.fctrYes
## -0.037204139 -0.042078733
## Q106389.fctrNo Q106997.fctrGr
## -0.069884472 -0.045946207
## Q106997.fctrYy Q107491.fctrYes
## 0.087529010 0.022505522
## Q108342.fctrOnline Q108855.fctrYes!
## 0.068972019 -0.048326214
## Q108950.fctrRisk-friendly Q109244.fctrNo
## 0.042397185 -0.374416559
## Q109244.fctrYes Q110740.fctrMac
## 0.763571824 0.016457805
## Q110740.fctrPC Q111220.fctrYes
## -0.095759349 0.102165768
## Q111848.fctrYes Q112270.fctrYes
## 0.029246498 0.002532131
## Q112478.fctrNo Q113181.fctrNo
## -0.071674308 0.094659726
## Q113181.fctrYes Q113992.fctrYes
## -0.101925125 0.018758347
## Q114152.fctrYes Q114386.fctrMysterious
## 0.001215794 0.005550234
## Q115390.fctrNo Q115390.fctrYes
## -0.085128214 0.024943706
## Q115602.fctrNo Q115611.fctrNo
## -0.006129422 0.121429244
## Q115611.fctrYes Q115899.fctrCs
## -0.328446834 0.082573783
## Q115899.fctrMe Q116197.fctrA.M.
## -0.012353330 -0.032450966
## Q116881.fctrHappy Q116881.fctrRight
## 0.078603456 -0.141878310
## Q116953.fctrNo Q116953.fctrYes
## -0.036656763 0.063262470
## Q117186.fctrHot headed Q117193.fctrStandard hours
## -0.019710980 -0.002981009
## Q118232.fctrId Q118233.fctrNo
## 0.120887721 -0.021333073
## Q118233.fctrYes Q119650.fctrGiving
## 0.014400900 -0.006146699
## Q119851.fctrNo Q119851.fctrYes
## -0.113773685 0.013486484
## Q120012.fctrYes Q120014.fctrNo
## 0.040890779 0.032195937
## Q120014.fctrYes Q120194.fctrStudy first
## -0.032533254 0.065256829
## Q120379.fctrNo Q120379.fctrYes
## -0.050149716 0.118837105
## Q120472.fctrScience Q120650.fctrYes
## -0.037775351 -0.030467057
## Q121699.fctrNo Q121699.fctrYes
## -0.051633489 0.044689201
## Q121700.fctrNo Q121700.fctrYes
## -0.011071443 0.016970195
## Q122120.fctrYes Q122771.fctrPt
## -0.024872502 -0.115816557
## Q123464.fctrNo Q124122.fctrNo
## -0.024443028 -0.034722635
## Q124122.fctrYes Q124742.fctrNo
## 0.004874931 0.039013147
## Q96024.fctrNo Q98059.fctrOnly-child
## 0.023646239 -0.009596390
## Q98059.fctrYes Q98197.fctrNo
## 0.073367383 0.184718092
## Q98197.fctrYes Q98578.fctrNo
## -0.083886484 -0.043820323
## Q98869.fctrNo Q99480.fctrNo
## 0.266626746 0.134742947
## Q99480.fctrYes YOB.Age.fctr.L
## -0.043472697 0.130623922
## YOB.Age.fctr.Q YOB.Age.fctr^4
## 0.036878348 0.054188683
## YOB.Age.fctr^6 YOB.Age.fctr^7
## 0.013010153 -0.049328694
## YOB.Age.fctr^8
## -0.071002509
## [1] "myfit_mdl: train diagnostics complete: 24.794000 secs"
## Prediction
## Reference R D
## R 1844 247
## D 1431 926
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.227518e-01 2.662403e-01 6.083184e-01 6.370239e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 5.105863e-36 2.163939e-183
## Prediction
## Reference R D
## R 476 50
## D 419 175
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.812500e-01 1.918521e-01 5.517282e-01 6.103439e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 3.471802e-04 9.306917e-65
## [1] "myfit_mdl: predict complete: 34.923000 secs"
## id
## 1 Low.cor.X##rcv#glmnet
## feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 23.305 2.128
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6534188 0.5872788 0.7195588 0.2783723
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6 0.6872903 0.6254518
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6083184 0.6370239 0.2446067
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6261026 0.526616 0.7255892 0.315743
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.65 0.6699507 0.58125
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5517282 0.6103439 0.1918521
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01290483 0.02676153
## [1] "myfit_mdl: exit: 34.938000 secs"fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 6 fit.models_0_Low.cor.X 1 5 glmnet 163.22 198.211
## 7 fit.models_0_end 1 6 teardown 198.22 NA
## elapsed
## 6 35
## 7 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 4 fit.models 4 0 0 124.554 198.235 73.681
## 5 fit.models 4 1 1 198.236 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 202.575 NA NA# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
major.inc = FALSE, label.minor = "setup")
indepVar <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)
dsp_models_df <- subset(dsp_models_df,
grepl(".glmnet", id, fixed = TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select important features
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <-
paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glbFeatsInteractionOnly)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df,
imp > imp_cutoff)), -1)
if (length(interact_vars) > 0) {
interact_vars <-
myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(interact_vars))
interact_vars <-
interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
}
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indepVar <- myextract_actual_feats(row.names(bst_featsimp_df))
indepVar <- setdiff(indepVar, topindep_var)
if (length(interact_vars) > 0) {
indepVar <-
setdiff(indepVar, myextract_actual_feats(interact_vars))
indepVar <- c(indepVar,
paste(topindep_var, setdiff(interact_vars, topindep_var),
sep = "*"))
} else indepVar <- union(indepVar, topindep_var)
}
}
if (is.null(indepVar))
indepVar <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indepVar) && grepl("RFE\\.", mdl_id_pfx))
indepVar <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indepVar))
indepVar <- mygetIndepVar(glb_feats_df)
if ((length(indepVar) == 1) && (grepl("^%<d-%", indepVar))) {
indepVar <-
eval(parse(text = str_trim(unlist(strsplit(indepVar, "%<d-%"))[2])))
}
indepVar <- myadjustInteractionFeats(glb_feats_df, indepVar)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
if (!is.null(glbMdlFamilies[["Best.Interact"]]))
glbMdlFamilies[[mdl_id_pfx]] <-
glbMdlFamilies[["Best.Interact"]]
}
}
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
mdl_methods <- glbMdlMethods else
mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indepVar <- setdiff(indepVar, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = fitobs_df, OOB_df = glbObsOOB)
# ntv_mdl <- glmnet(x = as.matrix(
# fitobs_df[, indepVar]),
# y = as.factor(as.character(
# fitobs_df[, glb_rsp_var])),
# family = "multinomial")
# bgn = 1; end = 100;
# ntv_mdl <- glmnet(x = as.matrix(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indepVar]),
# y = as.factor(as.character(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
# family = "multinomial")
}
}## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 202.575 202.586
## 2 fit.models_1_All.X 1 1 setup 202.586 NA
## elapsed
## 1 0.011
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 202.586 202.593
## 3 fit.models_1_All.X 1 2 glmnet 202.593 NA
## elapsed
## 2 0.007
## 3 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] " indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.731000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0534 on full training set
## [1] "myfit_mdl: train complete: 23.748000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 88 -none- numeric
## beta 20416 dgCMatrix S4
## df 88 -none- numeric
## dim 2 -none- numeric
## lambda 88 -none- numeric
## dev.ratio 88 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 232 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr^4
## 0.1920840842 -0.1005414034
## Edn.fctr^6 Edn.fctr^7
## 0.0334860119 0.0710425863
## Gender.fctrM Hhold.fctrMKy
## -0.0794064511 -0.1371778566
## Hhold.fctrPKn Hhold.fctrSKn
## 0.4997234624 0.0176790778
## Hhold.fctrSKy Income.fctr.Q
## 0.1011680811 -0.0816169876
## Income.fctr.C Income.fctr^4
## -0.1216519159 -0.0166494558
## Q100010.fctrNo Q100680.fctrYes
## 0.0094368536 0.0029292851
## Q100689.fctrYes Q101162.fctrPessimist
## 0.0884714668 -0.0071088785
## Q101163.fctrDad Q101163.fctrMom
## -0.1073473633 0.0759596110
## Q101596.fctrNo Q102687.fctrYes
## -0.0036990765 0.0309988067
## Q104996.fctrNo Q104996.fctrYes
## -0.0444993808 0.0096111358
## Q105655.fctrYes Q105840.fctrNo
## -0.0403102885 -0.0039386021
## Q106042.fctrNo Q106272.fctrYes
## -0.0351407304 -0.0370732762
## Q106389.fctrNo Q106997.fctrGr
## -0.0642024577 -0.0433734852
## Q106997.fctrYy Q107491.fctrYes
## 0.0814150590 0.0165630338
## Q108342.fctrOnline Q108855.fctrYes!
## 0.0631187924 -0.0426051502
## Q108950.fctrRisk-friendly Q109244.fctrNo
## 0.0371748796 -0.3676230271
## Q109244.fctrYes Q110740.fctrMac
## 0.7526090401 0.0158364523
## Q110740.fctrPC Q111220.fctrYes
## -0.0902880024 0.0957826931
## Q111848.fctrYes Q112478.fctrNo
## 0.0250589583 -0.0652435089
## Q113181.fctrNo Q113181.fctrYes
## 0.0925300327 -0.1002574917
## Q113992.fctrYes Q115390.fctrNo
## 0.0125173544 -0.0801709946
## Q115390.fctrYes Q115611.fctrNo
## 0.0226179572 0.1209408406
## Q115611.fctrYes Q115899.fctrCs
## -0.3209136716 0.0774284066
## Q115899.fctrMe Q116197.fctrA.M.
## -0.0123327062 -0.0248274337
## Q116881.fctrHappy Q116881.fctrRight
## 0.0742138891 -0.1383231722
## Q116953.fctrNo Q116953.fctrYes
## -0.0348182328 0.0562055276
## Q117186.fctrHot headed Q118232.fctrId
## -0.0145383547 0.1136211170
## Q118233.fctrNo Q118233.fctrYes
## -0.0170028152 0.0113463031
## Q119650.fctrGiving Q119851.fctrNo
## -0.0007454134 -0.1103781004
## Q119851.fctrYes Q120012.fctrYes
## 0.0120497740 0.0366728192
## Q120014.fctrNo Q120014.fctrYes
## 0.0280032695 -0.0299491592
## Q120194.fctrStudy first Q120379.fctrNo
## 0.0598036091 -0.0497973859
## Q120379.fctrYes Q120472.fctrScience
## 0.1111013195 -0.0359739528
## Q120650.fctrYes Q121699.fctrNo
## -0.0249600547 -0.0544691763
## Q121699.fctrYes Q121700.fctrNo
## 0.0374055344 -0.0072164530
## Q121700.fctrYes Q122120.fctrYes
## 0.0163285151 -0.0198345229
## Q122771.fctrPt Q123464.fctrNo
## -0.1085074600 -0.0188472361
## Q124122.fctrNo Q124122.fctrYes
## -0.0315353659 0.0006062715
## Q124742.fctrNo Q96024.fctrNo
## 0.0312573363 0.0189226203
## Q98059.fctrOnly-child Q98059.fctrYes
## -0.0023811587 0.0653704563
## Q98197.fctrNo Q98197.fctrYes
## 0.1793569818 -0.0826453929
## Q98578.fctrNo Q98869.fctrNo
## -0.0365491345 0.2587862534
## Q99480.fctrNo Q99480.fctrYes
## 0.1316394499 -0.0381190876
## YOB.Age.fctr.L YOB.Age.fctr.Q
## 0.1159818986 0.0236491601
## YOB.Age.fctr^4 YOB.Age.fctr^6
## 0.0449093485 0.0053748587
## YOB.Age.fctr^7 YOB.Age.fctr^8
## -0.0412125185 -0.0632525584
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr^4
## 0.190242606 -0.113726050
## Edn.fctr^6 Edn.fctr^7
## 0.039985577 0.076765149
## Gender.fctrM Hhold.fctrMKy
## -0.079499199 -0.139799243
## Hhold.fctrPKn Hhold.fctrSKn
## 0.516895044 0.023441414
## Hhold.fctrSKy Income.fctr.Q
## 0.112262855 -0.086764305
## Income.fctr.C Income.fctr^4
## -0.131008068 -0.022207230
## Income.fctr^6 Q100010.fctrNo
## 0.005184510 0.015575737
## Q100680.fctrYes Q100689.fctrYes
## 0.005695513 0.096910592
## Q101162.fctrPessimist Q101163.fctrDad
## -0.009462485 -0.112012131
## Q101163.fctrMom Q101596.fctrNo
## 0.077204753 -0.011035252
## Q102687.fctrYes Q103293.fctrNo
## 0.036506262 -0.003992889
## Q104996.fctrNo Q104996.fctrYes
## -0.046964218 0.013628231
## Q105655.fctrYes Q105840.fctrNo
## -0.045689342 -0.004398376
## Q106042.fctrNo Q106272.fctrYes
## -0.037204139 -0.042078733
## Q106389.fctrNo Q106997.fctrGr
## -0.069884472 -0.045946207
## Q106997.fctrYy Q107491.fctrYes
## 0.087529010 0.022505522
## Q108342.fctrOnline Q108855.fctrYes!
## 0.068972019 -0.048326214
## Q108950.fctrRisk-friendly Q109244.fctrNo
## 0.042397185 -0.374416559
## Q109244.fctrYes Q110740.fctrMac
## 0.763571824 0.016457805
## Q110740.fctrPC Q111220.fctrYes
## -0.095759349 0.102165768
## Q111848.fctrYes Q112270.fctrYes
## 0.029246498 0.002532131
## Q112478.fctrNo Q113181.fctrNo
## -0.071674308 0.094659726
## Q113181.fctrYes Q113992.fctrYes
## -0.101925125 0.018758347
## Q114152.fctrYes Q114386.fctrMysterious
## 0.001215794 0.005550234
## Q115390.fctrNo Q115390.fctrYes
## -0.085128214 0.024943706
## Q115602.fctrNo Q115611.fctrNo
## -0.006129422 0.121429244
## Q115611.fctrYes Q115899.fctrCs
## -0.328446834 0.082573783
## Q115899.fctrMe Q116197.fctrA.M.
## -0.012353330 -0.032450966
## Q116881.fctrHappy Q116881.fctrRight
## 0.078603456 -0.141878310
## Q116953.fctrNo Q116953.fctrYes
## -0.036656763 0.063262470
## Q117186.fctrHot headed Q117193.fctrStandard hours
## -0.019710980 -0.002981009
## Q118232.fctrId Q118233.fctrNo
## 0.120887721 -0.021333073
## Q118233.fctrYes Q119650.fctrGiving
## 0.014400900 -0.006146699
## Q119851.fctrNo Q119851.fctrYes
## -0.113773685 0.013486484
## Q120012.fctrYes Q120014.fctrNo
## 0.040890779 0.032195937
## Q120014.fctrYes Q120194.fctrStudy first
## -0.032533254 0.065256829
## Q120379.fctrNo Q120379.fctrYes
## -0.050149716 0.118837105
## Q120472.fctrScience Q120650.fctrYes
## -0.037775351 -0.030467057
## Q121699.fctrNo Q121699.fctrYes
## -0.051633489 0.044689201
## Q121700.fctrNo Q121700.fctrYes
## -0.011071443 0.016970195
## Q122120.fctrYes Q122771.fctrPt
## -0.024872502 -0.115816557
## Q123464.fctrNo Q124122.fctrNo
## -0.024443028 -0.034722635
## Q124122.fctrYes Q124742.fctrNo
## 0.004874931 0.039013147
## Q96024.fctrNo Q98059.fctrOnly-child
## 0.023646239 -0.009596390
## Q98059.fctrYes Q98197.fctrNo
## 0.073367383 0.184718092
## Q98197.fctrYes Q98578.fctrNo
## -0.083886484 -0.043820323
## Q98869.fctrNo Q99480.fctrNo
## 0.266626746 0.134742947
## Q99480.fctrYes YOB.Age.fctr.L
## -0.043472697 0.130623922
## YOB.Age.fctr.Q YOB.Age.fctr^4
## 0.036878348 0.054188683
## YOB.Age.fctr^6 YOB.Age.fctr^7
## 0.013010153 -0.049328694
## YOB.Age.fctr^8
## -0.071002509
## [1] "myfit_mdl: train diagnostics complete: 24.499000 secs"
## Prediction
## Reference R D
## R 1844 247
## D 1431 926
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.227518e-01 2.662403e-01 6.083184e-01 6.370239e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 5.105863e-36 2.163939e-183
## Prediction
## Reference R D
## R 476 50
## D 419 175
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.812500e-01 1.918521e-01 5.517282e-01 6.103439e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 3.471802e-04 9.306917e-65
## [1] "myfit_mdl: predict complete: 35.118000 secs"
## id
## 1 All.X##rcv#glmnet
## feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 22.908 2.054
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6534188 0.5872788 0.7195588 0.2783723
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6 0.6872903 0.6254518
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6083184 0.6370239 0.2446067
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.6261026 0.526616 0.7255892 0.315743
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.65 0.6699507 0.58125
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5517282 0.6103439 0.1918521
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01290483 0.02676153
## [1] "myfit_mdl: exit: 35.133000 secs"
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 202.593 237.732
## 4 fit.models_1_All.X 1 3 glm 237.733 NA
## elapsed
## 3 35.139
## 4 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] " indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.732000 secs"
## + Fold1.Rep1: parameter=none
## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none
## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none
## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none
## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none
## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none
## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none
## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none
## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 13.259000 secs"
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5365 -1.0394 0.4278 1.0349 2.3472
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4037443 0.2594431 1.556 0.119662
## .rnorm -0.0144735 0.0334725 -0.432 0.665451
## Edn.fctr.L -0.0853170 0.1551935 -0.550 0.582493
## Edn.fctr.Q -0.0145452 0.1458234 -0.100 0.920547
## Edn.fctr.C -0.0128867 0.1274767 -0.101 0.919479
## `Edn.fctr^4` -0.3442017 0.1257839 -2.736 0.006211 **
## `Edn.fctr^5` -0.0624099 0.1161193 -0.537 0.590947
## `Edn.fctr^6` 0.1421535 0.1052526 1.351 0.176826
## `Edn.fctr^7` 0.1957287 0.1162951 1.683 0.092368 .
## Gender.fctrF -0.3849892 0.2401749 -1.603 0.108945
## Gender.fctrM -0.4595950 0.2360077 -1.947 0.051490 .
## Hhold.fctrMKn 0.0340870 0.1822539 0.187 0.851637
## Hhold.fctrMKy -0.1340050 0.1684782 -0.795 0.426390
## Hhold.fctrPKn 0.9206637 0.2550394 3.610 0.000306 ***
## Hhold.fctrPKy 0.2295176 0.3396652 0.676 0.499220
## Hhold.fctrSKn 0.1955830 0.1425674 1.372 0.170106
## Hhold.fctrSKy 0.3657954 0.2459453 1.487 0.136935
## Income.fctr.L -0.1002526 0.1078238 -0.930 0.352484
## Income.fctr.Q -0.1751734 0.0989709 -1.770 0.076736 .
## Income.fctr.C -0.2569733 0.0962966 -2.669 0.007618 **
## `Income.fctr^4` -0.1112022 0.0936783 -1.187 0.235202
## `Income.fctr^5` -0.0004333 0.0952860 -0.005 0.996372
## `Income.fctr^6` 0.0910037 0.0926831 0.982 0.326159
## Q100010.fctrNo 0.2532892 0.2169400 1.168 0.242987
## Q100010.fctrYes 0.1846774 0.1986899 0.929 0.352643
## Q100562.fctrNo 0.0531004 0.2003390 0.265 0.790969
## Q100562.fctrYes 0.0619535 0.1763393 0.351 0.725340
## Q100680.fctrNo -0.2041623 0.1928685 -1.059 0.289802
## Q100680.fctrYes -0.1479846 0.1866402 -0.793 0.427844
## Q100689.fctrNo 0.3907495 0.1944442 2.010 0.044477 *
## Q100689.fctrYes 0.5517627 0.1930412 2.858 0.004260 **
## Q101162.fctrOptimist 0.0617722 0.1794274 0.344 0.730640
## Q101162.fctrPessimist 0.0407708 0.1853843 0.220 0.825929
## Q101163.fctrDad -0.2284776 0.1588094 -1.439 0.150238
## Q101163.fctrMom 0.0490395 0.1633177 0.300 0.763971
## Q101596.fctrNo -0.4893671 0.1629618 -3.003 0.002674 **
## Q101596.fctrYes -0.4440094 0.1722253 -2.578 0.009935 **
## Q102089.fctrOwn 0.1281066 0.1639400 0.781 0.434553
## Q102089.fctrRent 0.0916325 0.1733814 0.529 0.597151
## Q102289.fctrNo 0.1025436 0.1681116 0.610 0.541879
## Q102289.fctrYes 0.0543807 0.1789322 0.304 0.761190
## Q102674.fctrNo -0.4093310 0.2164253 -1.891 0.058581 .
## Q102674.fctrYes -0.3690017 0.2276504 -1.621 0.105036
## Q102687.fctrNo 0.4021434 0.2308784 1.742 0.081544 .
## Q102687.fctrYes 0.4752363 0.2290956 2.074 0.038042 *
## Q102906.fctrNo 0.0150782 0.1701992 0.089 0.929407
## Q102906.fctrYes -0.0164693 0.1747187 -0.094 0.924901
## Q103293.fctrNo -0.1000088 0.1526555 -0.655 0.512385
## Q103293.fctrYes 0.0027265 0.1545036 0.018 0.985921
## Q104996.fctrNo -0.0233455 0.1430365 -0.163 0.870350
## Q104996.fctrYes 0.1359172 0.1412363 0.962 0.335879
## Q105655.fctrNo -0.0865104 0.1739045 -0.497 0.618865
## Q105655.fctrYes -0.1804958 0.1719312 -1.050 0.293803
## Q105840.fctrNo 0.0802580 0.1755224 0.457 0.647490
## Q105840.fctrYes 0.0689533 0.1766436 0.390 0.696276
## Q106042.fctrNo -0.2406816 0.1740473 -1.383 0.166710
## Q106042.fctrYes -0.1831419 0.1743671 -1.050 0.293569
## Q106272.fctrNo 0.1568939 0.1948587 0.805 0.420723
## Q106272.fctrYes 0.0210004 0.1816784 0.116 0.907977
## Q106388.fctrNo -0.0278438 0.2133411 -0.131 0.896161
## Q106388.fctrYes -0.0084882 0.2254821 -0.038 0.969971
## Q106389.fctrNo -0.2556684 0.2119435 -1.206 0.227700
## Q106389.fctrYes -0.0851427 0.2135943 -0.399 0.690174
## Q106993.fctrNo -0.2186972 0.2150595 -1.017 0.309194
## Q106993.fctrYes -0.1047666 0.1913100 -0.548 0.583948
## Q106997.fctrGr 0.0158503 0.1928851 0.082 0.934508
## Q106997.fctrYy 0.2733245 0.1966022 1.390 0.164456
## Q107491.fctrNo 0.0908670 0.1815540 0.500 0.616726
## Q107491.fctrYes 0.1579099 0.1387772 1.138 0.255176
## Q107869.fctrNo 0.0062751 0.1460306 0.043 0.965725
## Q107869.fctrYes -0.0632948 0.1465056 -0.432 0.665719
## `Q108342.fctrIn-person` 0.2456134 0.1751802 1.402 0.160897
## Q108342.fctrOnline 0.3905191 0.1850827 2.110 0.034861 *
## Q108343.fctrNo -0.0932666 0.1815813 -0.514 0.607507
## Q108343.fctrYes -0.1535428 0.1918086 -0.800 0.423421
## Q108617.fctrNo 0.0681151 0.1659526 0.410 0.681477
## Q108617.fctrYes -0.1118839 0.2071519 -0.540 0.589124
## Q108754.fctrNo 0.0723608 0.1871754 0.387 0.699057
## Q108754.fctrYes 0.0610898 0.1957694 0.312 0.755003
## Q108855.fctrUmm... -0.0670437 0.2108132 -0.318 0.750466
## `Q108855.fctrYes!` -0.1883058 0.2071856 -0.909 0.363416
## Q108856.fctrSocialize -0.1926630 0.2135423 -0.902 0.366938
## Q108856.fctrSpace -0.1986380 0.1991624 -0.997 0.318586
## Q108950.fctrCautious 0.1185763 0.1558996 0.761 0.446900
## `Q108950.fctrRisk-friendly` 0.2304749 0.1672452 1.378 0.168183
## Q109244.fctrNo -0.5966868 0.1484292 -4.020 5.82e-05 ***
## Q109244.fctrYes 0.8423650 0.1706646 4.936 7.98e-07 ***
## Q109367.fctrNo 0.1198020 0.1535465 0.780 0.435254
## Q109367.fctrYes 0.0684263 0.1468452 0.466 0.641233
## Q110740.fctrMac -0.0158847 0.1298637 -0.122 0.902647
## Q110740.fctrPC -0.2236029 0.1267792 -1.764 0.077779 .
## Q111220.fctrNo -0.0142208 0.1396416 -0.102 0.918885
## Q111220.fctrYes 0.1857203 0.1531284 1.213 0.225191
## Q111580.fctrDemanding -0.0173339 0.1532867 -0.113 0.909966
## Q111580.fctrSupportive 0.0111346 0.1437728 0.077 0.938269
## Q111848.fctrNo 0.0949728 0.1517066 0.626 0.531296
## Q111848.fctrYes 0.1286246 0.1466950 0.877 0.380586
## Q112270.fctrNo 0.1390319 0.1419959 0.979 0.327518
## Q112270.fctrYes 0.1888612 0.1420612 1.329 0.183704
## Q112478.fctrNo -0.3660755 0.1736086 -2.109 0.034977 *
## Q112478.fctrYes -0.1532040 0.1676247 -0.914 0.360732
## Q112512.fctrNo 0.0859518 0.1838620 0.467 0.640157
## Q112512.fctrYes 0.0299766 0.1570307 0.191 0.848606
## Q113181.fctrNo 0.0799640 0.1377864 0.580 0.561680
## Q113181.fctrYes -0.1990248 0.1433051 -1.389 0.164888
## Q113583.fctrTalk 0.0900451 0.1977001 0.455 0.648776
## Q113583.fctrTunes 0.1246068 0.1898217 0.656 0.511540
## Q113584.fctrPeople -0.1411523 0.1942598 -0.727 0.467461
## Q113584.fctrTechnology -0.1176025 0.1930409 -0.609 0.542385
## Q113992.fctrNo 0.1943289 0.1550721 1.253 0.210151
## Q113992.fctrYes 0.2819251 0.1663777 1.694 0.090172 .
## Q114152.fctrNo -0.1271457 0.1520273 -0.836 0.402967
## Q114152.fctrYes -0.0101405 0.1633915 -0.062 0.950513
## Q114386.fctrMysterious 0.0662478 0.1534622 0.432 0.665968
## Q114386.fctrTMI -0.0128265 0.1569364 -0.082 0.934861
## Q114517.fctrNo 0.1943655 0.1666779 1.166 0.243568
## Q114517.fctrYes 0.2112956 0.1770188 1.194 0.232621
## Q114748.fctrNo -0.3312277 0.1768477 -1.873 0.061075 .
## Q114748.fctrYes -0.2977441 0.1750108 -1.701 0.088889 .
## Q114961.fctrNo 0.2182312 0.1694179 1.288 0.197703
## Q114961.fctrYes 0.1633945 0.1682321 0.971 0.331427
## Q115195.fctrNo 0.0691841 0.1662648 0.416 0.677331
## Q115195.fctrYes 0.1016507 0.1563161 0.650 0.515505
## Q115390.fctrNo -0.2192733 0.1499292 -1.463 0.143601
## Q115390.fctrYes -0.0018497 0.1404019 -0.013 0.989489
## Q115602.fctrNo 0.0695244 0.1931883 0.360 0.718938
## Q115602.fctrYes 0.1713497 0.1727895 0.992 0.321360
## Q115610.fctrNo -0.0570428 0.2041578 -0.279 0.779934
## Q115610.fctrYes -0.0522951 0.1805015 -0.290 0.772030
## Q115611.fctrNo -0.0194842 0.1903864 -0.102 0.918487
## Q115611.fctrYes -0.5824403 0.1955798 -2.978 0.002901 **
## Q115777.fctrEnd 0.0061494 0.1601912 0.038 0.969379
## Q115777.fctrStart 0.0562863 0.1562200 0.360 0.718622
## Q115899.fctrCs 0.2025209 0.1577967 1.283 0.199342
## Q115899.fctrMe 0.0173814 0.1556272 0.112 0.911072
## Q116197.fctrA.M. -0.3797977 0.1563614 -2.429 0.015142 *
## Q116197.fctrP.M. -0.2689386 0.1457797 -1.845 0.065062 .
## Q116441.fctrNo -0.1688255 0.1765961 -0.956 0.339073
## Q116441.fctrYes -0.0897363 0.1897777 -0.473 0.636320
## Q116448.fctrNo 0.1767473 0.1672473 1.057 0.290602
## Q116448.fctrYes 0.1345875 0.1687216 0.798 0.425051
## Q116601.fctrNo 0.2153571 0.1959974 1.099 0.271866
## Q116601.fctrYes 0.1869677 0.1675158 1.116 0.264371
## Q116797.fctrNo -0.1519075 0.1693141 -0.897 0.369616
## Q116797.fctrYes -0.1962125 0.1744885 -1.125 0.260801
## Q116881.fctrHappy 0.1403840 0.1646960 0.852 0.394002
## Q116881.fctrRight -0.1907077 0.1796898 -1.061 0.288546
## Q116953.fctrNo 0.0133781 0.1770131 0.076 0.939756
## Q116953.fctrYes 0.2574395 0.1666295 1.545 0.122351
## `Q117186.fctrCool headed` 0.0042746 0.1646871 0.026 0.979293
## `Q117186.fctrHot headed` -0.0816835 0.1728795 -0.472 0.636578
## `Q117193.fctrOdd hours` 0.0020904 0.1616737 0.013 0.989684
## `Q117193.fctrStandard hours` -0.0754438 0.1540240 -0.490 0.624263
## Q118117.fctrNo -0.0287136 0.1490744 -0.193 0.847262
## Q118117.fctrYes 0.0018835 0.1511387 0.012 0.990057
## Q118232.fctrId 0.4236049 0.1471877 2.878 0.004002 **
## Q118232.fctrPr 0.2290509 0.1454537 1.575 0.115318
## Q118233.fctrNo -0.1457718 0.1865583 -0.781 0.434583
## Q118233.fctrYes 0.0221315 0.2023382 0.109 0.912902
## Q118237.fctrNo -0.1548534 0.1894378 -0.817 0.413679
## Q118237.fctrYes -0.1295658 0.1862911 -0.696 0.486741
## Q118892.fctrNo 0.0909613 0.1321809 0.688 0.491354
## Q118892.fctrYes 0.0707765 0.1248520 0.567 0.570793
## Q119334.fctrNo -0.1277239 0.1366822 -0.934 0.350067
## Q119334.fctrYes -0.1034233 0.1332186 -0.776 0.437547
## Q119650.fctrGiving -0.1249624 0.1415136 -0.883 0.377214
## Q119650.fctrReceiving -0.0142925 0.1582289 -0.090 0.928026
## Q119851.fctrNo -0.1811673 0.1630867 -1.111 0.266626
## Q119851.fctrYes -0.0180694 0.1622614 -0.111 0.911331
## Q120012.fctrNo 0.0637899 0.1620091 0.394 0.693771
## Q120012.fctrYes 0.1659133 0.1607887 1.032 0.302132
## Q120014.fctrNo 0.0127211 0.1505837 0.084 0.932676
## Q120014.fctrYes -0.1240397 0.1429483 -0.868 0.385546
## `Q120194.fctrStudy first` 0.3176012 0.1386330 2.291 0.021966 *
## `Q120194.fctrTry first` 0.2164111 0.1440655 1.502 0.133053
## Q120379.fctrNo -0.0771023 0.1521128 -0.507 0.612242
## Q120379.fctrYes 0.2205305 0.1505211 1.465 0.142890
## Q120472.fctrArt -0.0612904 0.1547468 -0.396 0.692054
## Q120472.fctrScience -0.1324117 0.1443147 -0.918 0.358870
## Q120650.fctrNo -0.0517376 0.1965069 -0.263 0.792330
## Q120650.fctrYes -0.1897041 0.1440182 -1.317 0.187764
## Q120978.fctrNo 0.0561185 0.1581554 0.355 0.722716
## Q120978.fctrYes 0.0607749 0.1543980 0.394 0.693858
## Q121011.fctrNo 0.1730077 0.1586607 1.090 0.275526
## Q121011.fctrYes 0.1459248 0.1562375 0.934 0.350307
## Q121699.fctrNo 0.3917629 0.2430736 1.612 0.107026
## Q121699.fctrYes 0.5626445 0.2341963 2.402 0.016286 *
## Q121700.fctrNo -0.4484119 0.2365117 -1.896 0.057968 .
## Q121700.fctrYes -0.3703624 0.2552292 -1.451 0.146753
## Q122120.fctrNo -0.0470544 0.1382689 -0.340 0.733622
## Q122120.fctrYes -0.1324785 0.1520012 -0.872 0.383447
## Q122769.fctrNo -0.0711802 0.2106482 -0.338 0.735431
## Q122769.fctrYes -0.0706567 0.2137226 -0.331 0.740947
## Q122770.fctrNo 0.1791901 0.2564230 0.699 0.484673
## Q122770.fctrYes 0.1582678 0.2530170 0.626 0.531628
## Q122771.fctrPc -0.2038295 0.2347224 -0.868 0.385183
## Q122771.fctrPt -0.4152093 0.2487436 -1.669 0.095073 .
## Q123464.fctrNo -0.0806655 0.1597095 -0.505 0.613505
## Q123464.fctrYes 0.0799193 0.2336059 0.342 0.732267
## Q123621.fctrNo -0.0397879 0.1651916 -0.241 0.809665
## Q123621.fctrYes -0.0171433 0.1699050 -0.101 0.919630
## Q124122.fctrNo -0.0544581 0.1363018 -0.400 0.689495
## Q124122.fctrYes 0.1016767 0.1309076 0.777 0.437332
## Q124742.fctrNo 0.1625807 0.1045439 1.555 0.119912
## Q124742.fctrYes 0.0065147 0.1207972 0.054 0.956990
## Q96024.fctrNo 0.0891299 0.1331683 0.669 0.503303
## Q96024.fctrYes 0.0197989 0.1242295 0.159 0.873375
## `Q98059.fctrOnly-child` -0.0061363 0.2480548 -0.025 0.980264
## Q98059.fctrYes 0.2800269 0.2075198 1.349 0.177209
## Q98078.fctrNo -0.0854335 0.1918980 -0.445 0.656173
## Q98078.fctrYes -0.1305647 0.1945453 -0.671 0.502139
## Q98197.fctrNo 0.4035689 0.1876747 2.150 0.031526 *
## Q98197.fctrYes 0.0483067 0.1930565 0.250 0.802417
## Q98578.fctrNo -0.3931663 0.1556939 -2.525 0.011562 *
## Q98578.fctrYes -0.2830387 0.1628715 -1.738 0.082245 .
## Q98869.fctrNo 0.4837215 0.1689383 2.863 0.004192 **
## Q98869.fctrYes 0.0638411 0.1431705 0.446 0.655663
## Q99480.fctrNo 0.1611754 0.2028612 0.795 0.426898
## Q99480.fctrYes -0.1366070 0.1856640 -0.736 0.461867
## Q99581.fctrNo -0.2323245 0.2012039 -1.155 0.248225
## Q99581.fctrYes -0.1835486 0.2279284 -0.805 0.420652
## Q99716.fctrNo 0.2792082 0.1734253 1.610 0.107406
## Q99716.fctrYes 0.1833083 0.2223343 0.824 0.409672
## `Q99982.fctrCheck!` -0.2313003 0.1929679 -1.199 0.230665
## Q99982.fctrNope -0.1569335 0.1958894 -0.801 0.423054
## YOB.Age.fctr.L 0.4989675 0.1907924 2.615 0.008917 **
## YOB.Age.fctr.Q 0.2477063 0.1574487 1.573 0.115661
## YOB.Age.fctr.C -0.0613401 0.1363327 -0.450 0.652761
## `YOB.Age.fctr^4` 0.2440100 0.1273923 1.915 0.055439 .
## `YOB.Age.fctr^5` 0.0817560 0.1176802 0.695 0.487224
## `YOB.Age.fctr^6` 0.1388637 0.1061805 1.308 0.190939
## `YOB.Age.fctr^7` -0.1783184 0.1008120 -1.769 0.076924 .
## `YOB.Age.fctr^8` -0.2096555 0.1037623 -2.021 0.043328 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6150.3 on 4447 degrees of freedom
## Residual deviance: 5325.0 on 4215 degrees of freedom
## AIC: 5791
##
## Number of Fisher Scoring iterations: 4
##
## [1] "myfit_mdl: train diagnostics complete: 15.370000 secs"
## Prediction
## Reference R D
## R 1834 257
## D 1375 982
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.330935e-01 2.851214e-01 6.187337e-01 6.472784e-01 5.299011e-01
## AccuracyPValue McnemarPValue
## 4.069767e-44 2.800842e-168
## Prediction
## Reference R D
## R 483 43
## D 439 155
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.696429e-01 1.717908e-01 5.400481e-01 5.988709e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 4.539292e-03 2.260749e-72
## [1] "myfit_mdl: predict complete: 25.911000 secs"
## id
## 1 All.X##rcv#glm
## feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 12.416 1.27
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6685598 0.6413199 0.6957997 0.2624767
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.65 0.6920755 0.6049923
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6187337 0.6472784 0.2065762
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5989777 0.5380228 0.6599327 0.3371452
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.75 0.6671271 0.5696429
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5400481 0.5988709 0.1717908
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.007502422 0.01565638
## [1] "myfit_mdl: exit: 25.927000 secs"# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
label.minor = "preProc")## label step_major step_minor label_minor bgn end
## 4 fit.models_1_All.X 1 3 glm 237.733 263.716
## 5 fit.models_1_preProc 1 4 preProc 263.716 NA
## elapsed
## 4 25.983
## 5 NArequire(gdata)## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.sizemdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indepVar <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
"feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glbMdlTuneParams,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds,
trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method=method, train.preProcess=prePr)),
indepVar=indepVar, rsp_var=glb_rsp_var,
fit_df=fitobs_df, OOB_df=glbObsOOB)
}
# If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indepVar
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
# cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
# mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indepVar <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indepVar <- trim(unlist(strsplit(indepVar, "[,]")))
# indepVar <- setdiff(indepVar, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indepVar <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glbMdlMethods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indepVar, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indepVar_lst <- list()
# for (feat in setdiff(names(glbObsFit),
# union(glb_rsp_var, glbFeatsExclude)))
# indepVar_lst[["feat"]] <- feat
# User specified combinatorial models
# indepVar_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indepVar_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indepVar=indepVar,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
# model_loss_mtrx=glbMdlMetric_terms,
# model_summaryFunction=glbMdlMetricSummaryFn,
# model_metric=glbMdlMetricSummary,
# model_metric_maximize=glbMdlMetricMaximize)
# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glbObsFit, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glbMdlMetric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## MFO###myMFO_classfr .rnorm
## Random###myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr
## Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr
## Interact.High.cor.Y##rcv#glmnet Q109244.fctr,Gender.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q98078.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
## Low.cor.X##rcv#glmnet Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glmnet Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glm Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO###myMFO_classfr 0 0.386
## Random###myrandom_classfr 0 0.270
## Max.cor.Y.rcv.1X1###glmnet 0 0.790
## Max.cor.Y##rcv#rpart 5 1.566
## Interact.High.cor.Y##rcv#glmnet 25 5.276
## Low.cor.X##rcv#glmnet 25 23.305
## All.X##rcv#glmnet 25 22.908
## All.X##rcv#glm 1 12.416
## min.elapsedtime.final max.AUCpROC.fit
## MFO###myMFO_classfr 0.003 0.5000000
## Random###myrandom_classfr 0.002 0.4942483
## Max.cor.Y.rcv.1X1###glmnet 0.062 0.5971118
## Max.cor.Y##rcv#rpart 0.019 0.5971118
## Interact.High.cor.Y##rcv#glmnet 0.355 0.6184781
## Low.cor.X##rcv#glmnet 2.128 0.6534188
## All.X##rcv#glmnet 2.054 0.6534188
## All.X##rcv#glm 1.270 0.6685598
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr 0.0000000 1.0000000 0.5000000
## Random###myrandom_classfr 0.4619799 0.5265168 0.5073101
## Max.cor.Y.rcv.1X1###glmnet 0.5480631 0.6461604 0.3580613
## Max.cor.Y##rcv#rpart 0.5480631 0.6461604 0.3676308
## Interact.High.cor.Y##rcv#glmnet 0.5958871 0.6410692 0.3319465
## Low.cor.X##rcv#glmnet 0.5872788 0.7195588 0.2783723
## All.X##rcv#glmnet 0.5872788 0.7195588 0.2783723
## All.X##rcv#glm 0.6413199 0.6957997 0.2624767
## opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr 0.50 0.6395473
## Random###myrandom_classfr 0.55 0.6395473
## Max.cor.Y.rcv.1X1###glmnet 0.60 0.6720662
## Max.cor.Y##rcv#rpart 0.55 0.6720662
## Interact.High.cor.Y##rcv#glmnet 0.65 0.6727114
## Low.cor.X##rcv#glmnet 0.60 0.6872903
## All.X##rcv#glmnet 0.60 0.6872903
## All.X##rcv#glm 0.65 0.6920755
## max.Accuracy.fit max.AccuracyLower.fit
## MFO###myMFO_classfr 0.4700989 0.4553427
## Random###myrandom_classfr 0.4700989 0.4553427
## Max.cor.Y.rcv.1X1###glmnet 0.5721673 0.5574714
## Max.cor.Y##rcv#rpart 0.6000450 0.5574714
## Interact.High.cor.Y##rcv#glmnet 0.6058167 0.5633390
## Low.cor.X##rcv#glmnet 0.6254518 0.6083184
## All.X##rcv#glmnet 0.6254518 0.6083184
## All.X##rcv#glm 0.6049923 0.6187337
## max.AccuracyUpper.fit max.Kappa.fit
## MFO###myMFO_classfr 0.4848945 0.0000000
## Random###myrandom_classfr 0.4848945 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.5867683 0.1772539
## Max.cor.Y##rcv#rpart 0.5867683 0.1947896
## Interact.High.cor.Y##rcv#glmnet 0.5925837 0.2088694
## Low.cor.X##rcv#glmnet 0.6370239 0.2446067
## All.X##rcv#glmnet 0.6370239 0.2446067
## All.X##rcv#glm 0.6472784 0.2065762
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr 0.5000000 0.0000000 1.0000000
## Random###myrandom_classfr 0.5235690 0.5000000 0.5471380
## Max.cor.Y.rcv.1X1###glmnet 0.5896897 0.5228137 0.6565657
## Max.cor.Y##rcv#rpart 0.5896897 0.5228137 0.6565657
## Interact.High.cor.Y##rcv#glmnet 0.6031353 0.5665399 0.6397306
## Low.cor.X##rcv#glmnet 0.6261026 0.5266160 0.7255892
## All.X##rcv#glmnet 0.6261026 0.5266160 0.7255892
## All.X##rcv#glm 0.5989777 0.5380228 0.6599327
## max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr 0.5000000 0.50
## Random###myrandom_classfr 0.5191202 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.3658672 0.60
## Max.cor.Y##rcv#rpart 0.3774772 0.55
## Interact.High.cor.Y##rcv#glmnet 0.3571392 0.65
## Low.cor.X##rcv#glmnet 0.3157430 0.65
## All.X##rcv#glmnet 0.3157430 0.65
## All.X##rcv#glm 0.3371452 0.75
## max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr 0.6391252 0.4696429
## Random###myrandom_classfr 0.6391252 0.4696429
## Max.cor.Y.rcv.1X1###glmnet 0.6643789 0.5633929
## Max.cor.Y##rcv#rpart 0.6643789 0.5633929
## Interact.High.cor.Y##rcv#glmnet 0.6680556 0.5732143
## Low.cor.X##rcv#glmnet 0.6699507 0.5812500
## All.X##rcv#glmnet 0.6699507 0.5812500
## All.X##rcv#glm 0.6671271 0.5696429
## max.AccuracyLower.OOB
## MFO###myMFO_classfr 0.4400805
## Random###myrandom_classfr 0.4400805
## Max.cor.Y.rcv.1X1###glmnet 0.5337655
## Max.cor.Y##rcv#rpart 0.5337655
## Interact.High.cor.Y##rcv#glmnet 0.5436402
## Low.cor.X##rcv#glmnet 0.5517282
## All.X##rcv#glmnet 0.5517282
## All.X##rcv#glm 0.5400481
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO###myMFO_classfr 0.4993651 0.0000000
## Random###myrandom_classfr 0.4993651 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.5926864 0.1605510
## Max.cor.Y##rcv#rpart 0.5926864 0.1605510
## Interact.High.cor.Y##rcv#glmnet 0.6024028 0.1779779
## Low.cor.X##rcv#glmnet 0.6103439 0.1918521
## All.X##rcv#glmnet 0.6103439 0.1918521
## All.X##rcv#glm 0.5988709 0.1717908
## max.AccuracySD.fit max.KappaSD.fit
## MFO###myMFO_classfr NA NA
## Random###myrandom_classfr NA NA
## Max.cor.Y.rcv.1X1###glmnet NA NA
## Max.cor.Y##rcv#rpart 0.012403504 0.02559319
## Interact.High.cor.Y##rcv#glmnet 0.013121299 0.02732571
## Low.cor.X##rcv#glmnet 0.012904832 0.02676153
## All.X##rcv#glmnet 0.012904832 0.02676153
## All.X##rcv#glm 0.007502422 0.01565638rm(ret_lst)
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 5 fit.models_1_preProc 1 4 preProc 263.716 264.665
## 6 fit.models_1_end 1 5 teardown 264.665 NA
## elapsed
## 5 0.949
## 6 NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 5 fit.models 4 1 1 198.236 264.675 66.439
## 6 fit.models 4 2 2 264.676 NA NAfit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 268.776 NA NAplt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## MFO###myMFO_classfr .rnorm
## Random###myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr
## Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr
## Interact.High.cor.Y##rcv#glmnet Q109244.fctr,Gender.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q98078.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
## Low.cor.X##rcv#glmnet Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glmnet Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glm Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## max.nTuningRuns max.AUCpROC.fit
## MFO###myMFO_classfr 0 0.5000000
## Random###myrandom_classfr 0 0.4942483
## Max.cor.Y.rcv.1X1###glmnet 0 0.5971118
## Max.cor.Y##rcv#rpart 5 0.5971118
## Interact.High.cor.Y##rcv#glmnet 25 0.6184781
## Low.cor.X##rcv#glmnet 25 0.6534188
## All.X##rcv#glmnet 25 0.6534188
## All.X##rcv#glm 1 0.6685598
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr 0.0000000 1.0000000 0.5000000
## Random###myrandom_classfr 0.4619799 0.5265168 0.5073101
## Max.cor.Y.rcv.1X1###glmnet 0.5480631 0.6461604 0.3580613
## Max.cor.Y##rcv#rpart 0.5480631 0.6461604 0.3676308
## Interact.High.cor.Y##rcv#glmnet 0.5958871 0.6410692 0.3319465
## Low.cor.X##rcv#glmnet 0.5872788 0.7195588 0.2783723
## All.X##rcv#glmnet 0.5872788 0.7195588 0.2783723
## All.X##rcv#glm 0.6413199 0.6957997 0.2624767
## opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr 0.50 0.6395473
## Random###myrandom_classfr 0.55 0.6395473
## Max.cor.Y.rcv.1X1###glmnet 0.60 0.6720662
## Max.cor.Y##rcv#rpart 0.55 0.6720662
## Interact.High.cor.Y##rcv#glmnet 0.65 0.6727114
## Low.cor.X##rcv#glmnet 0.60 0.6872903
## All.X##rcv#glmnet 0.60 0.6872903
## All.X##rcv#glm 0.65 0.6920755
## max.Accuracy.fit max.Kappa.fit
## MFO###myMFO_classfr 0.4700989 0.0000000
## Random###myrandom_classfr 0.4700989 0.0000000
## Max.cor.Y.rcv.1X1###glmnet 0.5721673 0.1772539
## Max.cor.Y##rcv#rpart 0.6000450 0.1947896
## Interact.High.cor.Y##rcv#glmnet 0.6058167 0.2088694
## Low.cor.X##rcv#glmnet 0.6254518 0.2446067
## All.X##rcv#glmnet 0.6254518 0.2446067
## All.X##rcv#glm 0.6049923 0.2065762
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr 0.5000000 0.0000000 1.0000000
## Random###myrandom_classfr 0.5235690 0.5000000 0.5471380
## Max.cor.Y.rcv.1X1###glmnet 0.5896897 0.5228137 0.6565657
## Max.cor.Y##rcv#rpart 0.5896897 0.5228137 0.6565657
## Interact.High.cor.Y##rcv#glmnet 0.6031353 0.5665399 0.6397306
## Low.cor.X##rcv#glmnet 0.6261026 0.5266160 0.7255892
## All.X##rcv#glmnet 0.6261026 0.5266160 0.7255892
## All.X##rcv#glm 0.5989777 0.5380228 0.6599327
## max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr 0.5000000 0.50
## Random###myrandom_classfr 0.5191202 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.3658672 0.60
## Max.cor.Y##rcv#rpart 0.3774772 0.55
## Interact.High.cor.Y##rcv#glmnet 0.3571392 0.65
## Low.cor.X##rcv#glmnet 0.3157430 0.65
## All.X##rcv#glmnet 0.3157430 0.65
## All.X##rcv#glm 0.3371452 0.75
## max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr 0.6391252 0.4696429
## Random###myrandom_classfr 0.6391252 0.4696429
## Max.cor.Y.rcv.1X1###glmnet 0.6643789 0.5633929
## Max.cor.Y##rcv#rpart 0.6643789 0.5633929
## Interact.High.cor.Y##rcv#glmnet 0.6680556 0.5732143
## Low.cor.X##rcv#glmnet 0.6699507 0.5812500
## All.X##rcv#glmnet 0.6699507 0.5812500
## All.X##rcv#glm 0.6671271 0.5696429
## max.Kappa.OOB inv.elapsedtime.everything
## MFO###myMFO_classfr 0.0000000 2.59067358
## Random###myrandom_classfr 0.0000000 3.70370370
## Max.cor.Y.rcv.1X1###glmnet 0.1605510 1.26582278
## Max.cor.Y##rcv#rpart 0.1605510 0.63856960
## Interact.High.cor.Y##rcv#glmnet 0.1779779 0.18953753
## Low.cor.X##rcv#glmnet 0.1918521 0.04290925
## All.X##rcv#glmnet 0.1918521 0.04365287
## All.X##rcv#glm 0.1717908 0.08054124
## inv.elapsedtime.final
## MFO###myMFO_classfr 333.3333333
## Random###myrandom_classfr 500.0000000
## Max.cor.Y.rcv.1X1###glmnet 16.1290323
## Max.cor.Y##rcv#rpart 52.6315789
## Interact.High.cor.Y##rcv#glmnet 2.8169014
## Low.cor.X##rcv#glmnet 0.4699248
## All.X##rcv#glmnet 0.4868549
## All.X##rcv#glm 0.7874016# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 4 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 4 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])## id max.Accuracy.OOB max.AUCROCR.OOB
## 6 Low.cor.X##rcv#glmnet 0.5812500 0.3157430
## 7 All.X##rcv#glmnet 0.5812500 0.3157430
## 5 Interact.High.cor.Y##rcv#glmnet 0.5732143 0.3571392
## 8 All.X##rcv#glm 0.5696429 0.3371452
## 4 Max.cor.Y##rcv#rpart 0.5633929 0.3774772
## 3 Max.cor.Y.rcv.1X1###glmnet 0.5633929 0.3658672
## 2 Random###myrandom_classfr 0.4696429 0.5191202
## 1 MFO###myMFO_classfr 0.4696429 0.5000000
## max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 6 0.6261026 0.6254518 0.60
## 7 0.6261026 0.6254518 0.60
## 5 0.6031353 0.6058167 0.65
## 8 0.5989777 0.6049923 0.65
## 4 0.5896897 0.6000450 0.55
## 3 0.5896897 0.5721673 0.60
## 2 0.5235690 0.4700989 0.55
## 1 0.5000000 0.4700989 0.50
## opt.prob.threshold.OOB
## 6 0.65
## 7 0.65
## 5 0.65
## 8 0.75
## 4 0.55
## 3 0.60
## 2 0.55
## 1 0.50# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit -
## opt.prob.threshold.OOB
## <environment: 0x7fbc238a55b8>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: Low.cor.X##rcv#glmnet"glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value
predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var,
predct_var_name, predct_error_var_name)]
# tmp_obs_df <- obs_df %>%
# dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glbFeatsCategory) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glbFeatsCategory,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df,
paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE,
label.minor = "ensemble")
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
mygetEnsembleAutoMdlIds <- function() {
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
return(mdlIds[!grepl("Ensemble", mdlIds)])
}
if (glb_mdl_ensemble == "auto") {
glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
} else if (grepl("^%<d-%", glb_mdl_ensemble)) {
glb_mdl_ensemble <- eval(parse(text =
str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indepVar <- paste(indepVar, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indepVar <- intersect(indepVar, names(glbObsFit))
# indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
# if (glb_is_classification && glb_is_binomial)
# indepVar <- grep("prob$", indepVar, value=TRUE) else
# indepVar <- indepVar[!grepl("err$", indepVar)]
#rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
for (method in c("glm", "glmnet")) {
for (trainControlMethod in
c("boot", "boot632", "cv", "repeatedcv"
#, "LOOCV" # tuneLength * nrow(fitDF)
, "LGOCV", "adaptive_cv"
#, "adaptive_boot" #error: adaptive$min should be less than 3
#, "adaptive_LGOCV" #error: adaptive$min should be less than 3
)) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
if ((method == "glm") && (trainControlMethod != "repeatedcv"))
# glm used only to identify outliers
next
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod),
type = glb_model_type, tune.df = NULL,
trainControl.method = trainControlMethod,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
}
dsp_models_df <- get_dsp_models_df()
}
if (is.null(glbMdlSelId))
glbMdlSelId <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glbMdlSelId)) ## [1] "User specified selection: All.X##rcv#glmnet"myprint_mdl(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]])
## Length Class Mode
## a0 88 -none- numeric
## beta 20416 dgCMatrix S4
## df 88 -none- numeric
## dim 2 -none- numeric
## lambda 88 -none- numeric
## dev.ratio 88 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 232 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr^4
## 0.1920840842 -0.1005414034
## Edn.fctr^6 Edn.fctr^7
## 0.0334860119 0.0710425863
## Gender.fctrM Hhold.fctrMKy
## -0.0794064511 -0.1371778566
## Hhold.fctrPKn Hhold.fctrSKn
## 0.4997234624 0.0176790778
## Hhold.fctrSKy Income.fctr.Q
## 0.1011680811 -0.0816169876
## Income.fctr.C Income.fctr^4
## -0.1216519159 -0.0166494558
## Q100010.fctrNo Q100680.fctrYes
## 0.0094368536 0.0029292851
## Q100689.fctrYes Q101162.fctrPessimist
## 0.0884714668 -0.0071088785
## Q101163.fctrDad Q101163.fctrMom
## -0.1073473633 0.0759596110
## Q101596.fctrNo Q102687.fctrYes
## -0.0036990765 0.0309988067
## Q104996.fctrNo Q104996.fctrYes
## -0.0444993808 0.0096111358
## Q105655.fctrYes Q105840.fctrNo
## -0.0403102885 -0.0039386021
## Q106042.fctrNo Q106272.fctrYes
## -0.0351407304 -0.0370732762
## Q106389.fctrNo Q106997.fctrGr
## -0.0642024577 -0.0433734852
## Q106997.fctrYy Q107491.fctrYes
## 0.0814150590 0.0165630338
## Q108342.fctrOnline Q108855.fctrYes!
## 0.0631187924 -0.0426051502
## Q108950.fctrRisk-friendly Q109244.fctrNo
## 0.0371748796 -0.3676230271
## Q109244.fctrYes Q110740.fctrMac
## 0.7526090401 0.0158364523
## Q110740.fctrPC Q111220.fctrYes
## -0.0902880024 0.0957826931
## Q111848.fctrYes Q112478.fctrNo
## 0.0250589583 -0.0652435089
## Q113181.fctrNo Q113181.fctrYes
## 0.0925300327 -0.1002574917
## Q113992.fctrYes Q115390.fctrNo
## 0.0125173544 -0.0801709946
## Q115390.fctrYes Q115611.fctrNo
## 0.0226179572 0.1209408406
## Q115611.fctrYes Q115899.fctrCs
## -0.3209136716 0.0774284066
## Q115899.fctrMe Q116197.fctrA.M.
## -0.0123327062 -0.0248274337
## Q116881.fctrHappy Q116881.fctrRight
## 0.0742138891 -0.1383231722
## Q116953.fctrNo Q116953.fctrYes
## -0.0348182328 0.0562055276
## Q117186.fctrHot headed Q118232.fctrId
## -0.0145383547 0.1136211170
## Q118233.fctrNo Q118233.fctrYes
## -0.0170028152 0.0113463031
## Q119650.fctrGiving Q119851.fctrNo
## -0.0007454134 -0.1103781004
## Q119851.fctrYes Q120012.fctrYes
## 0.0120497740 0.0366728192
## Q120014.fctrNo Q120014.fctrYes
## 0.0280032695 -0.0299491592
## Q120194.fctrStudy first Q120379.fctrNo
## 0.0598036091 -0.0497973859
## Q120379.fctrYes Q120472.fctrScience
## 0.1111013195 -0.0359739528
## Q120650.fctrYes Q121699.fctrNo
## -0.0249600547 -0.0544691763
## Q121699.fctrYes Q121700.fctrNo
## 0.0374055344 -0.0072164530
## Q121700.fctrYes Q122120.fctrYes
## 0.0163285151 -0.0198345229
## Q122771.fctrPt Q123464.fctrNo
## -0.1085074600 -0.0188472361
## Q124122.fctrNo Q124122.fctrYes
## -0.0315353659 0.0006062715
## Q124742.fctrNo Q96024.fctrNo
## 0.0312573363 0.0189226203
## Q98059.fctrOnly-child Q98059.fctrYes
## -0.0023811587 0.0653704563
## Q98197.fctrNo Q98197.fctrYes
## 0.1793569818 -0.0826453929
## Q98578.fctrNo Q98869.fctrNo
## -0.0365491345 0.2587862534
## Q99480.fctrNo Q99480.fctrYes
## 0.1316394499 -0.0381190876
## YOB.Age.fctr.L YOB.Age.fctr.Q
## 0.1159818986 0.0236491601
## YOB.Age.fctr^4 YOB.Age.fctr^6
## 0.0449093485 0.0053748587
## YOB.Age.fctr^7 YOB.Age.fctr^8
## -0.0412125185 -0.0632525584
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr^4
## 0.190242606 -0.113726050
## Edn.fctr^6 Edn.fctr^7
## 0.039985577 0.076765149
## Gender.fctrM Hhold.fctrMKy
## -0.079499199 -0.139799243
## Hhold.fctrPKn Hhold.fctrSKn
## 0.516895044 0.023441414
## Hhold.fctrSKy Income.fctr.Q
## 0.112262855 -0.086764305
## Income.fctr.C Income.fctr^4
## -0.131008068 -0.022207230
## Income.fctr^6 Q100010.fctrNo
## 0.005184510 0.015575737
## Q100680.fctrYes Q100689.fctrYes
## 0.005695513 0.096910592
## Q101162.fctrPessimist Q101163.fctrDad
## -0.009462485 -0.112012131
## Q101163.fctrMom Q101596.fctrNo
## 0.077204753 -0.011035252
## Q102687.fctrYes Q103293.fctrNo
## 0.036506262 -0.003992889
## Q104996.fctrNo Q104996.fctrYes
## -0.046964218 0.013628231
## Q105655.fctrYes Q105840.fctrNo
## -0.045689342 -0.004398376
## Q106042.fctrNo Q106272.fctrYes
## -0.037204139 -0.042078733
## Q106389.fctrNo Q106997.fctrGr
## -0.069884472 -0.045946207
## Q106997.fctrYy Q107491.fctrYes
## 0.087529010 0.022505522
## Q108342.fctrOnline Q108855.fctrYes!
## 0.068972019 -0.048326214
## Q108950.fctrRisk-friendly Q109244.fctrNo
## 0.042397185 -0.374416559
## Q109244.fctrYes Q110740.fctrMac
## 0.763571824 0.016457805
## Q110740.fctrPC Q111220.fctrYes
## -0.095759349 0.102165768
## Q111848.fctrYes Q112270.fctrYes
## 0.029246498 0.002532131
## Q112478.fctrNo Q113181.fctrNo
## -0.071674308 0.094659726
## Q113181.fctrYes Q113992.fctrYes
## -0.101925125 0.018758347
## Q114152.fctrYes Q114386.fctrMysterious
## 0.001215794 0.005550234
## Q115390.fctrNo Q115390.fctrYes
## -0.085128214 0.024943706
## Q115602.fctrNo Q115611.fctrNo
## -0.006129422 0.121429244
## Q115611.fctrYes Q115899.fctrCs
## -0.328446834 0.082573783
## Q115899.fctrMe Q116197.fctrA.M.
## -0.012353330 -0.032450966
## Q116881.fctrHappy Q116881.fctrRight
## 0.078603456 -0.141878310
## Q116953.fctrNo Q116953.fctrYes
## -0.036656763 0.063262470
## Q117186.fctrHot headed Q117193.fctrStandard hours
## -0.019710980 -0.002981009
## Q118232.fctrId Q118233.fctrNo
## 0.120887721 -0.021333073
## Q118233.fctrYes Q119650.fctrGiving
## 0.014400900 -0.006146699
## Q119851.fctrNo Q119851.fctrYes
## -0.113773685 0.013486484
## Q120012.fctrYes Q120014.fctrNo
## 0.040890779 0.032195937
## Q120014.fctrYes Q120194.fctrStudy first
## -0.032533254 0.065256829
## Q120379.fctrNo Q120379.fctrYes
## -0.050149716 0.118837105
## Q120472.fctrScience Q120650.fctrYes
## -0.037775351 -0.030467057
## Q121699.fctrNo Q121699.fctrYes
## -0.051633489 0.044689201
## Q121700.fctrNo Q121700.fctrYes
## -0.011071443 0.016970195
## Q122120.fctrYes Q122771.fctrPt
## -0.024872502 -0.115816557
## Q123464.fctrNo Q124122.fctrNo
## -0.024443028 -0.034722635
## Q124122.fctrYes Q124742.fctrNo
## 0.004874931 0.039013147
## Q96024.fctrNo Q98059.fctrOnly-child
## 0.023646239 -0.009596390
## Q98059.fctrYes Q98197.fctrNo
## 0.073367383 0.184718092
## Q98197.fctrYes Q98578.fctrNo
## -0.083886484 -0.043820323
## Q98869.fctrNo Q99480.fctrNo
## 0.266626746 0.134742947
## Q99480.fctrYes YOB.Age.fctr.L
## -0.043472697 0.130623922
## YOB.Age.fctr.Q YOB.Age.fctr^4
## 0.036878348 0.054188683
## YOB.Age.fctr^6 YOB.Age.fctr^7
## 0.013010153 -0.049328694
## YOB.Age.fctr^8
## -0.071002509## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glbMdlSelId))## [1] "All.X##rcv#glmnet fit prediction diagnostics:"glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glbMdlSelId,
rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glbMdlSelId))## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glbMdlSelId,
rsp_var = glb_rsp_var)
print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))## All.X..rcv.glmnet.imp imp
## Q109244.fctrYes 100.0000000 100.0000000
## Hhold.fctrPKn 67.4470865 67.4470865
## Q109244.fctrNo 48.9989248 48.9989248
## Q115611.fctrYes 42.9430763 42.9430763
## Q98869.fctrNo 34.8166016 34.8166016
## Q98197.fctrNo 24.1226162 24.1226162
## Q116881.fctrRight 18.5423722 18.5423722
## Hhold.fctrMKy 18.2930135 18.2930135
## Q99480.fctrNo 17.6167561 17.6167561
## Income.fctr.C 16.9676954 16.9676954
## YOB.Age.fctr.L 16.7831959 16.7831959
## Q115611.fctrNo 15.9346193 15.9346193
## Q118232.fctrId 15.6916063 15.6916063
## Q120379.fctrYes 15.4104054 15.4104054
## Q122771.fctrPt 15.0245450 15.0245450
## Q119851.fctrNo 14.8555069 14.8555069
## Edn.fctr^4 14.6009991 14.6009991
## Q101163.fctrDad 14.5919797 14.5919797
## Hhold.fctrSKy 14.4618408 14.4618408
## Q113181.fctrYes 13.3432856 13.3432856
## Q111220.fctrYes 13.2553044 13.2553044
## Q100689.fctrYes 12.5130132 12.5130132
## Q110740.fctrPC 12.4370866 12.4370866
## Q113181.fctrNo 12.3774207 12.3774207
## Q106997.fctrYy 11.3399199 11.3399199
## Income.fctr.Q 11.2640070 11.2640070
## Q115390.fctrNo 11.0539640 11.0539640
## Q98197.fctrYes 10.9851323 10.9851323
## Q115899.fctrCs 10.7137252 10.7137252
## Gender.fctrM 10.4380830 10.4380830
## Q116881.fctrHappy 10.2114793 10.2114793
## Q101163.fctrMom 10.1075342 10.1075342
## Edn.fctr^7 9.9362540 9.9362540
## Q98059.fctrYes 9.4323555 9.4323555
## Q112478.fctrNo 9.2497254 9.2497254
## YOB.Age.fctr^8 9.1280457 9.1280457
## Q106389.fctrNo 9.0336598 9.0336598
## Q108342.fctrOnline 8.9094877 8.9094877
## Q120194.fctrStudy first 8.4317251 8.4317251
## Q116953.fctrYes 8.1291402 8.1291402
## YOB.Age.fctr^4 6.8811418 6.8811418
## Q121699.fctrNo 6.8528131 6.8528131
## Q120379.fctrNo 6.5771042 6.5771042
## YOB.Age.fctr^7 6.2723892 6.2723892
## Q108855.fctrYes! 6.2014763 6.2014763
## Q104996.fctrNo 6.1051871 6.1051871
## Q106997.fctrGr 5.9687582 5.9687582
## Q105655.fctrYes 5.8638558 5.8638558
## Q121699.fctrYes 5.6842085 5.6842085
## Q99480.fctrYes 5.5733945 5.5733945
## Q98578.fctrNo 5.5704173 5.5704173
## Q108950.fctrRisk-friendly 5.4354801 5.4354801
## Q106272.fctrYes 5.3991578 5.3991578
## Q120012.fctrYes 5.2631178 5.2631178
## Edn.fctr^6 5.0863780 5.0863780
## Q124742.fctrNo 4.9268126 4.9268126
## Q120472.fctrScience 4.9152606 4.9152606
## Q106042.fctrNo 4.8336001 4.8336001
## Q116953.fctrNo 4.7674175 4.7674175
## Q102687.fctrYes 4.6546081 4.6546081
## YOB.Age.fctr.Q 4.5076490 4.5076490
## Q124122.fctrNo 4.4792090 4.4792090
## Q120014.fctrYes 4.2069796 4.2069796
## Q120014.fctrNo 4.1218869 4.1218869
## Q116197.fctrA.M. 4.0683720 4.0683720
## Q120650.fctrYes 3.8615056 3.8615056
## Q111848.fctrYes 3.7346743 3.7346743
## Q115390.fctrYes 3.2168145 3.2168145
## Q122120.fctrYes 3.1386809 3.1386809
## Q123464.fctrNo 3.0681329 3.0681329
## Q96024.fctrNo 2.9856110 2.9856110
## Hhold.fctrSKn 2.9323698 2.9323698
## Q107491.fctrYes 2.8048927 2.8048927
## Income.fctr^4 2.7754752 2.7754752
## Q118233.fctrNo 2.6918042 2.6918042
## Q117186.fctrHot headed 2.4574162 2.4574162
## Q113992.fctrYes 2.3052153 2.3052153
## Q121700.fctrYes 2.2123810 2.2123810
## Q110740.fctrMac 2.1456057 2.1456057
## Q100010.fctrNo 1.8898404 1.8898404
## Q118233.fctrYes 1.8137700 1.8137700
## Q119851.fctrYes 1.7347117 1.7347117
## Q104996.fctrYes 1.6878881 1.6878881
## Q115899.fctrMe 1.6218096 1.6218096
## YOB.Age.fctr^6 1.5149593 1.5149593
## Q121700.fctrNo 1.3562224 1.3562224
## Q101596.fctrNo 1.2631861 1.2631861
## Q101162.fctrPessimist 1.1829972 1.1829972
## Q98059.fctrOnly-child 1.0772910 1.0772910
## Q100680.fctrYes 0.6778259 0.6778259
## Q119650.fctrGiving 0.6702535 0.6702535
## Q115602.fctrNo 0.6495190 0.6495190
## Q114386.fctrMysterious 0.5881439 0.5881439
## Q105840.fctrNo 0.5659680 0.5659680
## Income.fctr^6 0.5493890 0.5493890
## Q124122.fctrYes 0.5319589 0.5319589
## Q103293.fctrNo 0.4231161 0.4231161
## Q117193.fctrStandard hours 0.3158898 0.3158898
## Q112270.fctrYes 0.2683233 0.2683233
## Q114152.fctrYes 0.1288345 0.1288345
## .rnorm 0.0000000 0.0000000
## Edn.fctr.L 0.0000000 0.0000000
## Edn.fctr.Q 0.0000000 0.0000000
## Edn.fctr.C 0.0000000 0.0000000
## Edn.fctr^5 0.0000000 0.0000000
## Gender.fctrF 0.0000000 0.0000000
## Hhold.fctrMKn 0.0000000 0.0000000
## Hhold.fctrPKy 0.0000000 0.0000000
## Income.fctr.L 0.0000000 0.0000000
## Income.fctr^5 0.0000000 0.0000000
## Q100010.fctrYes 0.0000000 0.0000000
## Q100562.fctrNo 0.0000000 0.0000000
## Q100562.fctrYes 0.0000000 0.0000000
## Q100680.fctrNo 0.0000000 0.0000000
## Q100689.fctrNo 0.0000000 0.0000000
## Q101162.fctrOptimist 0.0000000 0.0000000
## Q101596.fctrYes 0.0000000 0.0000000
## Q102089.fctrOwn 0.0000000 0.0000000
## Q102089.fctrRent 0.0000000 0.0000000
## Q102289.fctrNo 0.0000000 0.0000000
## Q102289.fctrYes 0.0000000 0.0000000
## Q102674.fctrNo 0.0000000 0.0000000
## Q102674.fctrYes 0.0000000 0.0000000
## Q102687.fctrNo 0.0000000 0.0000000
## Q102906.fctrNo 0.0000000 0.0000000
## Q102906.fctrYes 0.0000000 0.0000000
## Q103293.fctrYes 0.0000000 0.0000000
## Q105655.fctrNo 0.0000000 0.0000000
## Q105840.fctrYes 0.0000000 0.0000000
## Q106042.fctrYes 0.0000000 0.0000000
## Q106272.fctrNo 0.0000000 0.0000000
## Q106388.fctrNo 0.0000000 0.0000000
## Q106388.fctrYes 0.0000000 0.0000000
## Q106389.fctrYes 0.0000000 0.0000000
## Q106993.fctrNo 0.0000000 0.0000000
## Q106993.fctrYes 0.0000000 0.0000000
## Q107491.fctrNo 0.0000000 0.0000000
## Q107869.fctrNo 0.0000000 0.0000000
## Q107869.fctrYes 0.0000000 0.0000000
## Q108342.fctrIn-person 0.0000000 0.0000000
## Q108343.fctrNo 0.0000000 0.0000000
## Q108343.fctrYes 0.0000000 0.0000000
## Q108617.fctrNo 0.0000000 0.0000000
## Q108617.fctrYes 0.0000000 0.0000000
## Q108754.fctrNo 0.0000000 0.0000000
## Q108754.fctrYes 0.0000000 0.0000000
## Q108855.fctrUmm... 0.0000000 0.0000000
## Q108856.fctrSocialize 0.0000000 0.0000000
## Q108856.fctrSpace 0.0000000 0.0000000
## Q108950.fctrCautious 0.0000000 0.0000000
## Q109367.fctrNo 0.0000000 0.0000000
## Q109367.fctrYes 0.0000000 0.0000000
## Q111220.fctrNo 0.0000000 0.0000000
## Q111580.fctrDemanding 0.0000000 0.0000000
## Q111580.fctrSupportive 0.0000000 0.0000000
## Q111848.fctrNo 0.0000000 0.0000000
## Q112270.fctrNo 0.0000000 0.0000000
## Q112478.fctrYes 0.0000000 0.0000000
## Q112512.fctrNo 0.0000000 0.0000000
## Q112512.fctrYes 0.0000000 0.0000000
## Q113583.fctrTalk 0.0000000 0.0000000
## Q113583.fctrTunes 0.0000000 0.0000000
## Q113584.fctrPeople 0.0000000 0.0000000
## Q113584.fctrTechnology 0.0000000 0.0000000
## Q113992.fctrNo 0.0000000 0.0000000
## Q114152.fctrNo 0.0000000 0.0000000
## Q114386.fctrTMI 0.0000000 0.0000000
## Q114517.fctrNo 0.0000000 0.0000000
## Q114517.fctrYes 0.0000000 0.0000000
## Q114748.fctrNo 0.0000000 0.0000000
## Q114748.fctrYes 0.0000000 0.0000000
## Q114961.fctrNo 0.0000000 0.0000000
## Q114961.fctrYes 0.0000000 0.0000000
## Q115195.fctrNo 0.0000000 0.0000000
## Q115195.fctrYes 0.0000000 0.0000000
## Q115602.fctrYes 0.0000000 0.0000000
## Q115610.fctrNo 0.0000000 0.0000000
## Q115610.fctrYes 0.0000000 0.0000000
## Q115777.fctrEnd 0.0000000 0.0000000
## Q115777.fctrStart 0.0000000 0.0000000
## Q116197.fctrP.M. 0.0000000 0.0000000
## Q116441.fctrNo 0.0000000 0.0000000
## Q116441.fctrYes 0.0000000 0.0000000
## Q116448.fctrNo 0.0000000 0.0000000
## Q116448.fctrYes 0.0000000 0.0000000
## Q116601.fctrNo 0.0000000 0.0000000
## Q116601.fctrYes 0.0000000 0.0000000
## Q116797.fctrNo 0.0000000 0.0000000
## Q116797.fctrYes 0.0000000 0.0000000
## Q117186.fctrCool headed 0.0000000 0.0000000
## Q117193.fctrOdd hours 0.0000000 0.0000000
## Q118117.fctrNo 0.0000000 0.0000000
## Q118117.fctrYes 0.0000000 0.0000000
## Q118232.fctrPr 0.0000000 0.0000000
## Q118237.fctrNo 0.0000000 0.0000000
## Q118237.fctrYes 0.0000000 0.0000000
## Q118892.fctrNo 0.0000000 0.0000000
## Q118892.fctrYes 0.0000000 0.0000000
## Q119334.fctrNo 0.0000000 0.0000000
## Q119334.fctrYes 0.0000000 0.0000000
## Q119650.fctrReceiving 0.0000000 0.0000000
## Q120012.fctrNo 0.0000000 0.0000000
## Q120194.fctrTry first 0.0000000 0.0000000
## Q120472.fctrArt 0.0000000 0.0000000
## Q120650.fctrNo 0.0000000 0.0000000
## Q120978.fctrNo 0.0000000 0.0000000
## Q120978.fctrYes 0.0000000 0.0000000
## Q121011.fctrNo 0.0000000 0.0000000
## Q121011.fctrYes 0.0000000 0.0000000
## Q122120.fctrNo 0.0000000 0.0000000
## Q122769.fctrNo 0.0000000 0.0000000
## Q122769.fctrYes 0.0000000 0.0000000
## Q122770.fctrNo 0.0000000 0.0000000
## Q122770.fctrYes 0.0000000 0.0000000
## Q122771.fctrPc 0.0000000 0.0000000
## Q123464.fctrYes 0.0000000 0.0000000
## Q123621.fctrNo 0.0000000 0.0000000
## Q123621.fctrYes 0.0000000 0.0000000
## Q124742.fctrYes 0.0000000 0.0000000
## Q96024.fctrYes 0.0000000 0.0000000
## Q98078.fctrNo 0.0000000 0.0000000
## Q98078.fctrYes 0.0000000 0.0000000
## Q98578.fctrYes 0.0000000 0.0000000
## Q98869.fctrYes 0.0000000 0.0000000
## Q99581.fctrNo 0.0000000 0.0000000
## Q99581.fctrYes 0.0000000 0.0000000
## Q99716.fctrNo 0.0000000 0.0000000
## Q99716.fctrYes 0.0000000 0.0000000
## Q99982.fctrCheck! 0.0000000 0.0000000
## Q99982.fctrNope 0.0000000 0.0000000
## YOB.Age.fctr.C 0.0000000 0.0000000
## YOB.Age.fctr^5 0.0000000 0.0000000#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <-
ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -imp.max,
summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(imp.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ",
nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars = var,
measure.vars = c(glb_rsp_var, rsp_var_out))
print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
facet_colcol_name = "variable", jitter = TRUE) +
guides(color = FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glbFeatsId)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
require(lazyeval)
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df = obs_df,
feat_x = ifelse(nrow(featsimp_df) > 1,
featsimp_df$feat[2], ".rownames"),
feat_y = featsimp_df$feat[1],
rsp_var = glb_rsp_var,
rsp_var_out = rsp_var_out,
id_vars = glbFeatsId,
prob_threshold = prob_threshold))
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
prob_threshold = glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 107
## Loading required package: lazyeval
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 1393 D 0.2330384
## 2 2798 D 0.2333508
## 3 4075 D 0.2351937
## 4 1843 D 0.2499408
## 5 1187 D 0.2510454
## 6 1045 D 0.2563875
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 R TRUE
## 2 R TRUE
## 3 R TRUE
## 4 R TRUE
## 5 R TRUE
## 6 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.7669616 FALSE
## 2 0.7666492 FALSE
## 3 0.7648063 FALSE
## 4 0.7500592 FALSE
## 5 0.7489546 FALSE
## 6 0.7436125 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1 FALSE -0.4169616
## 2 FALSE -0.4166492
## 3 FALSE -0.4148063
## 4 FALSE -0.4000592
## 5 FALSE -0.3989546
## 6 FALSE -0.3936125
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 12 1230 D 0.2839466
## 99 3957 D 0.4465035
## 193 4171 D 0.5180425
## 235 4002 D 0.5375024
## 288 5416 D 0.5542771
## 421 6343 R 0.6540221
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 12 R TRUE
## 99 R TRUE
## 193 R TRUE
## 235 R TRUE
## 288 R TRUE
## 421 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 12 0.7160534
## 99 0.5534965
## 193 0.4819575
## 235 0.4624976
## 288 0.4457229
## 421 0.6540221
## Party.fctr.All.X..rcv.glmnet.is.acc
## 12 FALSE
## 99 FALSE
## 193 FALSE
## 235 FALSE
## 288 FALSE
## 421 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 12 FALSE
## 99 FALSE
## 193 FALSE
## 235 FALSE
## 288 FALSE
## 421 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 12 -0.36605344
## 99 -0.20349647
## 193 -0.13195753
## 235 -0.11249756
## 288 -0.09572293
## 421 0.00402211
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 464 5148 R 0.8307342
## 465 1118 R 0.8371768
## 466 4010 R 0.8416294
## 467 3921 R 0.8597362
## 468 1307 R 0.8832304
## 469 451 R 0.8853558
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 464 D TRUE
## 465 D TRUE
## 466 D TRUE
## 467 D TRUE
## 468 D TRUE
## 469 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 464 0.8307342
## 465 0.8371768
## 466 0.8416294
## 467 0.8597362
## 468 0.8832304
## 469 0.8853558
## Party.fctr.All.X..rcv.glmnet.is.acc
## 464 FALSE
## 465 FALSE
## 466 FALSE
## 467 FALSE
## 468 FALSE
## 469 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 464 FALSE
## 465 FALSE
## 466 FALSE
## 467 FALSE
## 468 FALSE
## 469 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 464 0.1807342
## 465 0.1871768
## 466 0.1916294
## 467 0.2097362
## 468 0.2332304
## 469 0.2353558
if (!is.null(glbFeatsCategory)) {
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsFit, mdl_id = glbMdlSelId,
label = "fit"),
by = glbFeatsCategory, all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
label="OOB"),
#by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}## Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## PKy PKy 9 52 10 0.01169065 0.008035714
## PKn PKn 30 150 37 0.03372302 0.026785714
## N N 83 367 102 0.08250899 0.074107143
## SKn SKn 511 1920 638 0.43165468 0.456250000
## MKn MKn 136 516 169 0.11600719 0.121428571
## SKy SKy 53 147 65 0.03304856 0.047321429
## MKy MKy 298 1296 371 0.29136691 0.266071429
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## PKy 0.007183908 24.55182 0.4721503 52 4.44135
## PKn 0.026580460 54.28673 0.3619116 150 14.22010
## N 0.073275862 169.17762 0.4609745 367 37.99992
## SKn 0.458333333 855.61896 0.4456349 1920 233.08138
## MKn 0.121408046 227.85830 0.4415859 516 61.65902
## SKy 0.046695402 61.84682 0.4207267 147 23.64664
## MKy 0.266522989 572.19199 0.4415062 1296 130.77361
## err.abs.OOB.mean
## PKy 0.4934833
## PKn 0.4740034
## N 0.4578304
## SKn 0.4561280
## MKn 0.4533752
## SKy 0.4461630
## MKy 0.4388376
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 1120.000000 4448.000000 1392.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 1965.532248 3.044490
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 4448.000000 505.822031 3.219821write.csv(glbObsOOB[, c(glbFeatsId,
grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glbOut$pfx, glbMdlSelId), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 277.348 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 6 fit.models 4 2 2 264.676 277.359 12.683
## 7 fit.models 4 3 3 277.359 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 7 fit.models 4 3 3 277.359 281.996
## 8 fit.data.training 5 0 0 281.996 NA
## elapsed
## 7 4.637
## 8 NAStep 5.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{
warning("Final model same as glbMdlSelId")
glbMdlFinId <- paste0("Final.", glbMdlSelId)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
mdlDf$id <- glbMdlFinId
glb_models_df <- rbind(glb_models_df, mdlDf)
} else {
if (grepl("RFE\\.X", names(glbMdlFamilies))) {
indepVar <- mygetIndepVar(glb_feats_df)
rfe_trn_results <-
myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
sort(predictors(rfe_fit_results))))) {
print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
}
}
# }
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
# Fit selected models on glbObsTrn
for (mdl_id in gsub(".prob", "",
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
predictors(rfe_trn_results))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indepVar = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL)
glbObsTrn <- glb_get_predictions(df = glbObsTrn,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glbObsNew <- glb_get_predictions(df = glbObsNew,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
if (glb_is_classification && glb_is_binomial)
indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else
if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
indepVar <- myextract_actual_feats(predictors(rfe_trn_results))
} else indepVar <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glbMdlSelId
, "feats"], "[,]")))
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.",
paste(glb_preproc_methods, collapse = "|"),
fixed = TRUE), glbMdlSelId)) != -1))
ths_preProcess <- str_sub(glbMdlSelId, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
"Final.Ensemble", "Final")
trnobs_df <- glbObsTrn
if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
}
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indepVar) == 1) && (method %in% "glmnet"))
next
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = ths_preProcess)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = trnobs_df, OOB_df = NULL)
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glbMdlFinId <- glb_models_df[length(glb_models_lst), "id"]
}
}
}## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Final##rcv#glmnet"
## [1] " indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.692000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.0113 on full training set
## [1] "myfit_mdl: train complete: 28.033000 secs"
## Length Class Mode
## a0 77 -none- numeric
## beta 17864 dgCMatrix S4
## df 77 -none- numeric
## dim 2 -none- numeric
## lambda 77 -none- numeric
## dev.ratio 77 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 232 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.L Gender.fctrM
## 2.123071e-01 2.157532e-02 -1.089012e-01
## Hhold.fctrMKy Hhold.fctrPKn Income.fctr.Q
## -8.164986e-02 3.776989e-01 -5.982145e-02
## Income.fctr.C Q100689.fctrYes Q101163.fctrDad
## -6.845478e-02 7.776009e-02 -9.435015e-02
## Q101163.fctrMom Q104996.fctrNo Q106042.fctrNo
## 5.478042e-02 -2.122253e-02 -2.683896e-02
## Q106389.fctrNo Q106997.fctrGr Q108855.fctrYes!
## -2.242088e-02 -7.229662e-02 -2.697942e-02
## Q109244.fctrNo Q109244.fctrYes Q110740.fctrMac
## -3.570162e-01 9.252278e-01 2.173880e-02
## Q110740.fctrPC Q112478.fctrNo Q113181.fctrNo
## -7.110575e-02 -3.234141e-02 1.047847e-01
## Q113181.fctrYes Q115195.fctrYes Q115390.fctrNo
## -1.442422e-01 1.409950e-02 -6.846845e-03
## Q115390.fctrYes Q115611.fctrNo Q115611.fctrYes
## 5.491365e-02 1.353109e-01 -3.314197e-01
## Q115899.fctrCs Q116881.fctrHappy Q116881.fctrRight
## 4.819157e-02 1.619212e-02 -1.668260e-01
## Q116953.fctrNo Q118232.fctrId Q118233.fctrNo
## -3.153005e-02 9.756745e-02 -3.955244e-03
## Q119851.fctrNo Q120194.fctrStudy first Q120379.fctrNo
## -1.006952e-01 4.033322e-02 -1.266726e-02
## Q120379.fctrYes Q120472.fctrScience Q120650.fctrYes
## 8.628830e-02 -8.052739e-02 -7.918516e-05
## Q121699.fctrYes Q122120.fctrYes Q122771.fctrPt
## 3.237186e-02 -1.457223e-02 -7.566506e-02
## Q124742.fctrNo Q98197.fctrNo Q98197.fctrYes
## 8.531459e-03 2.842728e-01 -3.409862e-03
## Q98869.fctrNo Q99480.fctrNo Q99480.fctrYes
## 1.875869e-01 3.711351e-02 -4.728446e-02
## YOB.Age.fctr.L YOB.Age.fctr^7 YOB.Age.fctr^8
## 6.134774e-02 -1.275503e-02 -3.263560e-02
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.L Gender.fctrM
## 0.2194026415 0.0250789647 -0.1110166523
## Hhold.fctrMKy Hhold.fctrPKn Income.fctr.Q
## -0.0922968501 0.3888002722 -0.0675789374
## Income.fctr.C Q100689.fctrYes Q101163.fctrDad
## -0.0815213744 0.0873664932 -0.0968737805
## Q101163.fctrMom Q104996.fctrNo Q106042.fctrNo
## 0.0605524619 -0.0294158307 -0.0293613758
## Q106389.fctrNo Q106997.fctrGr Q108855.fctrYes!
## -0.0310950390 -0.0813603310 -0.0347190523
## Q109244.fctrNo Q109244.fctrYes Q110740.fctrMac
## -0.3582630105 0.9302958326 0.0289083519
## Q110740.fctrPC Q111220.fctrYes Q112478.fctrNo
## -0.0732343770 0.0047213847 -0.0404980916
## Q113181.fctrNo Q113181.fctrYes Q113583.fctrTunes
## 0.1096475233 -0.1426961792 0.0001044441
## Q115195.fctrYes Q115390.fctrNo Q115390.fctrYes
## 0.0225819717 -0.0163258587 0.0569504027
## Q115611.fctrNo Q115611.fctrYes Q115899.fctrCs
## 0.1359076104 -0.3370943566 0.0566104909
## Q116881.fctrHappy Q116881.fctrRight Q116953.fctrNo
## 0.0256811888 -0.1671335987 -0.0421907026
## Q118232.fctrId Q118233.fctrNo Q119851.fctrNo
## 0.1082336575 -0.0147693511 -0.1069967848
## Q120194.fctrStudy first Q120379.fctrNo Q120379.fctrYes
## 0.0499363227 -0.0112236709 0.0989672666
## Q120472.fctrScience Q120650.fctrYes Q121699.fctrYes
## -0.0870280043 -0.0114635573 0.0410727129
## Q122120.fctrYes Q122771.fctrPt Q124742.fctrNo
## -0.0240492262 -0.0868530842 0.0215413569
## Q98197.fctrNo Q98869.fctrNo Q99480.fctrNo
## 0.2931645340 0.1970976751 0.0382915209
## Q99480.fctrYes YOB.Age.fctr.L YOB.Age.fctr^7
## -0.0578658541 0.0762294190 -0.0256109530
## YOB.Age.fctr^8
## -0.0451630163
## [1] "myfit_mdl: train diagnostics complete: 28.698000 secs"
## Prediction
## Reference R D
## R 2323 294
## D 1902 1049
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.056034e-01 2.349603e-01 5.926242e-01 6.184720e-01 5.299928e-01
## AccuracyPValue McnemarPValue
## 3.833186e-30 1.013594e-257
## [1] "myfit_mdl: predict complete: 36.135000 secs"
## id
## 1 Final##rcv#glmnet
## feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 27.204 2.304
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6424822 0.5716469 0.7133175 0.2937147
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6 0.6790412 0.6343978
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5926242 0.618472 0.2626432
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01458328 0.02942434
## [1] "myfit_mdl: exit: 36.151000 secs"rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 8 fit.data.training 5 0 0 281.996 318.737
## 9 fit.data.training 5 1 1 318.738 NA
## elapsed
## 8 36.741
## 9 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFinId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFinId)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.65glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Q109244.fctrYes 100.0000000 1.000000e+02
## Hhold.fctrPKn 67.4470865 4.111360e+01
## Q109244.fctrNo 48.9989248 3.856398e+01
## Q115611.fctrYes 42.9430763 3.594481e+01
## Q98197.fctrNo 24.1226162 3.096120e+01
## Q98869.fctrNo 34.8166016 2.054829e+01
## Q116881.fctrRight 18.5423722 1.801125e+01
## Q113181.fctrYes 13.3432856 1.551456e+01
## Q115611.fctrNo 15.9346193 1.461994e+01
## Gender.fctrM 10.4380830 1.181920e+01
## Q113181.fctrNo 12.3774207 1.146362e+01
## Q119851.fctrNo 14.8555069 1.106875e+01
## Q118232.fctrId 15.6916063 1.087203e+01
## Q101163.fctrDad 14.5919797 1.026223e+01
## Q120379.fctrYes 15.4104054 9.719875e+00
## Hhold.fctrMKy 18.2930135 9.153825e+00
## Q120472.fctrScience 4.9152606 8.898966e+00
## Q100689.fctrYes 12.5130132 8.700537e+00
## Q122771.fctrPt 15.0245450 8.525487e+00
## Q106997.fctrGr 5.9687582 8.093498e+00
## Income.fctr.C 16.9676954 7.808056e+00
## Q110740.fctrPC 12.4370866 7.741310e+00
## YOB.Age.fctr.L 16.7831959 7.099717e+00
## Income.fctr.Q 11.2640070 6.705236e+00
## Q101163.fctrMom 10.1075342 6.097245e+00
## Q115390.fctrYes 3.2168145 5.991141e+00
## Q115899.fctrCs 10.7137252 5.471650e+00
## Q99480.fctrYes 5.5733945 5.443518e+00
## Q120194.fctrStudy first 8.4317251 4.661891e+00
## Q99480.fctrNo 17.6167561 4.042722e+00
## YOB.Age.fctr^8 9.1280457 3.925603e+00
## Q121699.fctrYes 5.6842085 3.773720e+00
## Q112478.fctrNo 9.2497254 3.752883e+00
## Q116953.fctrNo 4.7674175 3.746097e+00
## Q108855.fctrYes! 6.2014763 3.160846e+00
## Q106042.fctrNo 4.8336001 2.977412e+00
## Q106389.fctrNo 9.0336598 2.699101e+00
## Q110740.fctrMac 2.1456057 2.576971e+00
## Q104996.fctrNo 6.1051871 2.554284e+00
## Edn.fctr.L 0.0000000 2.441089e+00
## Q116881.fctrHappy 10.2114793 2.053272e+00
## Q122120.fctrYes 3.1386809 1.878088e+00
## Q115195.fctrYes 0.0000000 1.795001e+00
## YOB.Age.fctr^7 6.2723892 1.790989e+00
## Q124742.fctrNo 4.9268126 1.340212e+00
## Q120379.fctrNo 6.5771042 1.320296e+00
## Q115390.fctrNo 11.0539640 1.044548e+00
## Q118233.fctrNo 2.6918042 7.755926e-01
## Q120650.fctrYes 3.8615056 3.757408e-01
## Q98197.fctrYes 10.9851323 2.579574e-01
## Q111220.fctrYes 13.2553044 1.522855e-01
## Q113583.fctrTunes 0.0000000 3.368785e-03
## .rnorm 0.0000000 0.000000e+00
## Edn.fctr.C 0.0000000 0.000000e+00
## Edn.fctr.Q 0.0000000 0.000000e+00
## Edn.fctr^4 14.6009991 0.000000e+00
## Edn.fctr^5 0.0000000 0.000000e+00
## Edn.fctr^6 5.0863780 0.000000e+00
## Edn.fctr^7 9.9362540 0.000000e+00
## Gender.fctrF 0.0000000 0.000000e+00
## Hhold.fctrMKn 0.0000000 0.000000e+00
## Hhold.fctrPKy 0.0000000 0.000000e+00
## Hhold.fctrSKn 2.9323698 0.000000e+00
## Hhold.fctrSKy 14.4618408 0.000000e+00
## Income.fctr.L 0.0000000 0.000000e+00
## Income.fctr^4 2.7754752 0.000000e+00
## Income.fctr^5 0.0000000 0.000000e+00
## Income.fctr^6 0.5493890 0.000000e+00
## Q100010.fctrNo 1.8898404 0.000000e+00
## Q100010.fctrYes 0.0000000 0.000000e+00
## Q100562.fctrNo 0.0000000 0.000000e+00
## Q100562.fctrYes 0.0000000 0.000000e+00
## Q100680.fctrNo 0.0000000 0.000000e+00
## Q100680.fctrYes 0.6778259 0.000000e+00
## Q100689.fctrNo 0.0000000 0.000000e+00
## Q101162.fctrOptimist 0.0000000 0.000000e+00
## Q101162.fctrPessimist 1.1829972 0.000000e+00
## Q101596.fctrNo 1.2631861 0.000000e+00
## Q101596.fctrYes 0.0000000 0.000000e+00
## Q102089.fctrOwn 0.0000000 0.000000e+00
## Q102089.fctrRent 0.0000000 0.000000e+00
## Q102289.fctrNo 0.0000000 0.000000e+00
## Q102289.fctrYes 0.0000000 0.000000e+00
## Q102674.fctrNo 0.0000000 0.000000e+00
## Q102674.fctrYes 0.0000000 0.000000e+00
## Q102687.fctrNo 0.0000000 0.000000e+00
## Q102687.fctrYes 4.6546081 0.000000e+00
## Q102906.fctrNo 0.0000000 0.000000e+00
## Q102906.fctrYes 0.0000000 0.000000e+00
## Q103293.fctrNo 0.4231161 0.000000e+00
## Q103293.fctrYes 0.0000000 0.000000e+00
## Q104996.fctrYes 1.6878881 0.000000e+00
## Q105655.fctrNo 0.0000000 0.000000e+00
## Q105655.fctrYes 5.8638558 0.000000e+00
## Q105840.fctrNo 0.5659680 0.000000e+00
## Q105840.fctrYes 0.0000000 0.000000e+00
## Q106042.fctrYes 0.0000000 0.000000e+00
## Q106272.fctrNo 0.0000000 0.000000e+00
## Q106272.fctrYes 5.3991578 0.000000e+00
## Q106388.fctrNo 0.0000000 0.000000e+00
## Q106388.fctrYes 0.0000000 0.000000e+00
## Q106389.fctrYes 0.0000000 0.000000e+00
## Q106993.fctrNo 0.0000000 0.000000e+00
## Q106993.fctrYes 0.0000000 0.000000e+00
## Q106997.fctrYy 11.3399199 0.000000e+00
## Q107491.fctrNo 0.0000000 0.000000e+00
## Q107491.fctrYes 2.8048927 0.000000e+00
## Q107869.fctrNo 0.0000000 0.000000e+00
## Q107869.fctrYes 0.0000000 0.000000e+00
## Q108342.fctrIn-person 0.0000000 0.000000e+00
## Q108342.fctrOnline 8.9094877 0.000000e+00
## Q108343.fctrNo 0.0000000 0.000000e+00
## Q108343.fctrYes 0.0000000 0.000000e+00
## Q108617.fctrNo 0.0000000 0.000000e+00
## Q108617.fctrYes 0.0000000 0.000000e+00
## Q108754.fctrNo 0.0000000 0.000000e+00
## Q108754.fctrYes 0.0000000 0.000000e+00
## Q108855.fctrUmm... 0.0000000 0.000000e+00
## Q108856.fctrSocialize 0.0000000 0.000000e+00
## Q108856.fctrSpace 0.0000000 0.000000e+00
## Q108950.fctrCautious 0.0000000 0.000000e+00
## Q108950.fctrRisk-friendly 5.4354801 0.000000e+00
## Q109367.fctrNo 0.0000000 0.000000e+00
## Q109367.fctrYes 0.0000000 0.000000e+00
## Q111220.fctrNo 0.0000000 0.000000e+00
## Q111580.fctrDemanding 0.0000000 0.000000e+00
## Q111580.fctrSupportive 0.0000000 0.000000e+00
## Q111848.fctrNo 0.0000000 0.000000e+00
## Q111848.fctrYes 3.7346743 0.000000e+00
## Q112270.fctrNo 0.0000000 0.000000e+00
## Q112270.fctrYes 0.2683233 0.000000e+00
## Q112478.fctrYes 0.0000000 0.000000e+00
## Q112512.fctrNo 0.0000000 0.000000e+00
## Q112512.fctrYes 0.0000000 0.000000e+00
## Q113583.fctrTalk 0.0000000 0.000000e+00
## Q113584.fctrPeople 0.0000000 0.000000e+00
## Q113584.fctrTechnology 0.0000000 0.000000e+00
## Q113992.fctrNo 0.0000000 0.000000e+00
## Q113992.fctrYes 2.3052153 0.000000e+00
## Q114152.fctrNo 0.0000000 0.000000e+00
## Q114152.fctrYes 0.1288345 0.000000e+00
## Q114386.fctrMysterious 0.5881439 0.000000e+00
## Q114386.fctrTMI 0.0000000 0.000000e+00
## Q114517.fctrNo 0.0000000 0.000000e+00
## Q114517.fctrYes 0.0000000 0.000000e+00
## Q114748.fctrNo 0.0000000 0.000000e+00
## Q114748.fctrYes 0.0000000 0.000000e+00
## Q114961.fctrNo 0.0000000 0.000000e+00
## Q114961.fctrYes 0.0000000 0.000000e+00
## Q115195.fctrNo 0.0000000 0.000000e+00
## Q115602.fctrNo 0.6495190 0.000000e+00
## Q115602.fctrYes 0.0000000 0.000000e+00
## Q115610.fctrNo 0.0000000 0.000000e+00
## Q115610.fctrYes 0.0000000 0.000000e+00
## Q115777.fctrEnd 0.0000000 0.000000e+00
## Q115777.fctrStart 0.0000000 0.000000e+00
## Q115899.fctrMe 1.6218096 0.000000e+00
## Q116197.fctrA.M. 4.0683720 0.000000e+00
## Q116197.fctrP.M. 0.0000000 0.000000e+00
## Q116441.fctrNo 0.0000000 0.000000e+00
## Q116441.fctrYes 0.0000000 0.000000e+00
## Q116448.fctrNo 0.0000000 0.000000e+00
## Q116448.fctrYes 0.0000000 0.000000e+00
## Q116601.fctrNo 0.0000000 0.000000e+00
## Q116601.fctrYes 0.0000000 0.000000e+00
## Q116797.fctrNo 0.0000000 0.000000e+00
## Q116797.fctrYes 0.0000000 0.000000e+00
## Q116953.fctrYes 8.1291402 0.000000e+00
## Q117186.fctrCool headed 0.0000000 0.000000e+00
## Q117186.fctrHot headed 2.4574162 0.000000e+00
## Q117193.fctrOdd hours 0.0000000 0.000000e+00
## Q117193.fctrStandard hours 0.3158898 0.000000e+00
## Q118117.fctrNo 0.0000000 0.000000e+00
## Q118117.fctrYes 0.0000000 0.000000e+00
## Q118232.fctrPr 0.0000000 0.000000e+00
## Q118233.fctrYes 1.8137700 0.000000e+00
## Q118237.fctrNo 0.0000000 0.000000e+00
## Q118237.fctrYes 0.0000000 0.000000e+00
## Q118892.fctrNo 0.0000000 0.000000e+00
## Q118892.fctrYes 0.0000000 0.000000e+00
## Q119334.fctrNo 0.0000000 0.000000e+00
## Q119334.fctrYes 0.0000000 0.000000e+00
## Q119650.fctrGiving 0.6702535 0.000000e+00
## Q119650.fctrReceiving 0.0000000 0.000000e+00
## Q119851.fctrYes 1.7347117 0.000000e+00
## Q120012.fctrNo 0.0000000 0.000000e+00
## Q120012.fctrYes 5.2631178 0.000000e+00
## Q120014.fctrNo 4.1218869 0.000000e+00
## Q120014.fctrYes 4.2069796 0.000000e+00
## Q120194.fctrTry first 0.0000000 0.000000e+00
## Q120472.fctrArt 0.0000000 0.000000e+00
## Q120650.fctrNo 0.0000000 0.000000e+00
## Q120978.fctrNo 0.0000000 0.000000e+00
## Q120978.fctrYes 0.0000000 0.000000e+00
## Q121011.fctrNo 0.0000000 0.000000e+00
## Q121011.fctrYes 0.0000000 0.000000e+00
## Q121699.fctrNo 6.8528131 0.000000e+00
## Q121700.fctrNo 1.3562224 0.000000e+00
## Q121700.fctrYes 2.2123810 0.000000e+00
## Q122120.fctrNo 0.0000000 0.000000e+00
## Q122769.fctrNo 0.0000000 0.000000e+00
## Q122769.fctrYes 0.0000000 0.000000e+00
## Q122770.fctrNo 0.0000000 0.000000e+00
## Q122770.fctrYes 0.0000000 0.000000e+00
## Q122771.fctrPc 0.0000000 0.000000e+00
## Q123464.fctrNo 3.0681329 0.000000e+00
## Q123464.fctrYes 0.0000000 0.000000e+00
## Q123621.fctrNo 0.0000000 0.000000e+00
## Q123621.fctrYes 0.0000000 0.000000e+00
## Q124122.fctrNo 4.4792090 0.000000e+00
## Q124122.fctrYes 0.5319589 0.000000e+00
## Q124742.fctrYes 0.0000000 0.000000e+00
## Q96024.fctrNo 2.9856110 0.000000e+00
## Q96024.fctrYes 0.0000000 0.000000e+00
## Q98059.fctrOnly-child 1.0772910 0.000000e+00
## Q98059.fctrYes 9.4323555 0.000000e+00
## Q98078.fctrNo 0.0000000 0.000000e+00
## Q98078.fctrYes 0.0000000 0.000000e+00
## Q98578.fctrNo 5.5704173 0.000000e+00
## Q98578.fctrYes 0.0000000 0.000000e+00
## Q98869.fctrYes 0.0000000 0.000000e+00
## Q99581.fctrNo 0.0000000 0.000000e+00
## Q99581.fctrYes 0.0000000 0.000000e+00
## Q99716.fctrNo 0.0000000 0.000000e+00
## Q99716.fctrYes 0.0000000 0.000000e+00
## Q99982.fctrCheck! 0.0000000 0.000000e+00
## Q99982.fctrNope 0.0000000 0.000000e+00
## YOB.Age.fctr.C 0.0000000 0.000000e+00
## YOB.Age.fctr.Q 4.5076490 0.000000e+00
## YOB.Age.fctr^4 6.8811418 0.000000e+00
## YOB.Age.fctr^5 0.0000000 0.000000e+00
## YOB.Age.fctr^6 1.5149593 0.000000e+00
## imp
## Q109244.fctrYes 1.000000e+02
## Hhold.fctrPKn 4.111360e+01
## Q109244.fctrNo 3.856398e+01
## Q115611.fctrYes 3.594481e+01
## Q98197.fctrNo 3.096120e+01
## Q98869.fctrNo 2.054829e+01
## Q116881.fctrRight 1.801125e+01
## Q113181.fctrYes 1.551456e+01
## Q115611.fctrNo 1.461994e+01
## Gender.fctrM 1.181920e+01
## Q113181.fctrNo 1.146362e+01
## Q119851.fctrNo 1.106875e+01
## Q118232.fctrId 1.087203e+01
## Q101163.fctrDad 1.026223e+01
## Q120379.fctrYes 9.719875e+00
## Hhold.fctrMKy 9.153825e+00
## Q120472.fctrScience 8.898966e+00
## Q100689.fctrYes 8.700537e+00
## Q122771.fctrPt 8.525487e+00
## Q106997.fctrGr 8.093498e+00
## Income.fctr.C 7.808056e+00
## Q110740.fctrPC 7.741310e+00
## YOB.Age.fctr.L 7.099717e+00
## Income.fctr.Q 6.705236e+00
## Q101163.fctrMom 6.097245e+00
## Q115390.fctrYes 5.991141e+00
## Q115899.fctrCs 5.471650e+00
## Q99480.fctrYes 5.443518e+00
## Q120194.fctrStudy first 4.661891e+00
## Q99480.fctrNo 4.042722e+00
## YOB.Age.fctr^8 3.925603e+00
## Q121699.fctrYes 3.773720e+00
## Q112478.fctrNo 3.752883e+00
## Q116953.fctrNo 3.746097e+00
## Q108855.fctrYes! 3.160846e+00
## Q106042.fctrNo 2.977412e+00
## Q106389.fctrNo 2.699101e+00
## Q110740.fctrMac 2.576971e+00
## Q104996.fctrNo 2.554284e+00
## Edn.fctr.L 2.441089e+00
## Q116881.fctrHappy 2.053272e+00
## Q122120.fctrYes 1.878088e+00
## Q115195.fctrYes 1.795001e+00
## YOB.Age.fctr^7 1.790989e+00
## Q124742.fctrNo 1.340212e+00
## Q120379.fctrNo 1.320296e+00
## Q115390.fctrNo 1.044548e+00
## Q118233.fctrNo 7.755926e-01
## Q120650.fctrYes 3.757408e-01
## Q98197.fctrYes 2.579574e-01
## Q111220.fctrYes 1.522855e-01
## Q113583.fctrTunes 3.368785e-03
## .rnorm 0.000000e+00
## Edn.fctr.C 0.000000e+00
## Edn.fctr.Q 0.000000e+00
## Edn.fctr^4 0.000000e+00
## Edn.fctr^5 0.000000e+00
## Edn.fctr^6 0.000000e+00
## Edn.fctr^7 0.000000e+00
## Gender.fctrF 0.000000e+00
## Hhold.fctrMKn 0.000000e+00
## Hhold.fctrPKy 0.000000e+00
## Hhold.fctrSKn 0.000000e+00
## Hhold.fctrSKy 0.000000e+00
## Income.fctr.L 0.000000e+00
## Income.fctr^4 0.000000e+00
## Income.fctr^5 0.000000e+00
## Income.fctr^6 0.000000e+00
## Q100010.fctrNo 0.000000e+00
## Q100010.fctrYes 0.000000e+00
## Q100562.fctrNo 0.000000e+00
## Q100562.fctrYes 0.000000e+00
## Q100680.fctrNo 0.000000e+00
## Q100680.fctrYes 0.000000e+00
## Q100689.fctrNo 0.000000e+00
## Q101162.fctrOptimist 0.000000e+00
## Q101162.fctrPessimist 0.000000e+00
## Q101596.fctrNo 0.000000e+00
## Q101596.fctrYes 0.000000e+00
## Q102089.fctrOwn 0.000000e+00
## Q102089.fctrRent 0.000000e+00
## Q102289.fctrNo 0.000000e+00
## Q102289.fctrYes 0.000000e+00
## Q102674.fctrNo 0.000000e+00
## Q102674.fctrYes 0.000000e+00
## Q102687.fctrNo 0.000000e+00
## Q102687.fctrYes 0.000000e+00
## Q102906.fctrNo 0.000000e+00
## Q102906.fctrYes 0.000000e+00
## Q103293.fctrNo 0.000000e+00
## Q103293.fctrYes 0.000000e+00
## Q104996.fctrYes 0.000000e+00
## Q105655.fctrNo 0.000000e+00
## Q105655.fctrYes 0.000000e+00
## Q105840.fctrNo 0.000000e+00
## Q105840.fctrYes 0.000000e+00
## Q106042.fctrYes 0.000000e+00
## Q106272.fctrNo 0.000000e+00
## Q106272.fctrYes 0.000000e+00
## Q106388.fctrNo 0.000000e+00
## Q106388.fctrYes 0.000000e+00
## Q106389.fctrYes 0.000000e+00
## Q106993.fctrNo 0.000000e+00
## Q106993.fctrYes 0.000000e+00
## Q106997.fctrYy 0.000000e+00
## Q107491.fctrNo 0.000000e+00
## Q107491.fctrYes 0.000000e+00
## Q107869.fctrNo 0.000000e+00
## Q107869.fctrYes 0.000000e+00
## Q108342.fctrIn-person 0.000000e+00
## Q108342.fctrOnline 0.000000e+00
## Q108343.fctrNo 0.000000e+00
## Q108343.fctrYes 0.000000e+00
## Q108617.fctrNo 0.000000e+00
## Q108617.fctrYes 0.000000e+00
## Q108754.fctrNo 0.000000e+00
## Q108754.fctrYes 0.000000e+00
## Q108855.fctrUmm... 0.000000e+00
## Q108856.fctrSocialize 0.000000e+00
## Q108856.fctrSpace 0.000000e+00
## Q108950.fctrCautious 0.000000e+00
## Q108950.fctrRisk-friendly 0.000000e+00
## Q109367.fctrNo 0.000000e+00
## Q109367.fctrYes 0.000000e+00
## Q111220.fctrNo 0.000000e+00
## Q111580.fctrDemanding 0.000000e+00
## Q111580.fctrSupportive 0.000000e+00
## Q111848.fctrNo 0.000000e+00
## Q111848.fctrYes 0.000000e+00
## Q112270.fctrNo 0.000000e+00
## Q112270.fctrYes 0.000000e+00
## Q112478.fctrYes 0.000000e+00
## Q112512.fctrNo 0.000000e+00
## Q112512.fctrYes 0.000000e+00
## Q113583.fctrTalk 0.000000e+00
## Q113584.fctrPeople 0.000000e+00
## Q113584.fctrTechnology 0.000000e+00
## Q113992.fctrNo 0.000000e+00
## Q113992.fctrYes 0.000000e+00
## Q114152.fctrNo 0.000000e+00
## Q114152.fctrYes 0.000000e+00
## Q114386.fctrMysterious 0.000000e+00
## Q114386.fctrTMI 0.000000e+00
## Q114517.fctrNo 0.000000e+00
## Q114517.fctrYes 0.000000e+00
## Q114748.fctrNo 0.000000e+00
## Q114748.fctrYes 0.000000e+00
## Q114961.fctrNo 0.000000e+00
## Q114961.fctrYes 0.000000e+00
## Q115195.fctrNo 0.000000e+00
## Q115602.fctrNo 0.000000e+00
## Q115602.fctrYes 0.000000e+00
## Q115610.fctrNo 0.000000e+00
## Q115610.fctrYes 0.000000e+00
## Q115777.fctrEnd 0.000000e+00
## Q115777.fctrStart 0.000000e+00
## Q115899.fctrMe 0.000000e+00
## Q116197.fctrA.M. 0.000000e+00
## Q116197.fctrP.M. 0.000000e+00
## Q116441.fctrNo 0.000000e+00
## Q116441.fctrYes 0.000000e+00
## Q116448.fctrNo 0.000000e+00
## Q116448.fctrYes 0.000000e+00
## Q116601.fctrNo 0.000000e+00
## Q116601.fctrYes 0.000000e+00
## Q116797.fctrNo 0.000000e+00
## Q116797.fctrYes 0.000000e+00
## Q116953.fctrYes 0.000000e+00
## Q117186.fctrCool headed 0.000000e+00
## Q117186.fctrHot headed 0.000000e+00
## Q117193.fctrOdd hours 0.000000e+00
## Q117193.fctrStandard hours 0.000000e+00
## Q118117.fctrNo 0.000000e+00
## Q118117.fctrYes 0.000000e+00
## Q118232.fctrPr 0.000000e+00
## Q118233.fctrYes 0.000000e+00
## Q118237.fctrNo 0.000000e+00
## Q118237.fctrYes 0.000000e+00
## Q118892.fctrNo 0.000000e+00
## Q118892.fctrYes 0.000000e+00
## Q119334.fctrNo 0.000000e+00
## Q119334.fctrYes 0.000000e+00
## Q119650.fctrGiving 0.000000e+00
## Q119650.fctrReceiving 0.000000e+00
## Q119851.fctrYes 0.000000e+00
## Q120012.fctrNo 0.000000e+00
## Q120012.fctrYes 0.000000e+00
## Q120014.fctrNo 0.000000e+00
## Q120014.fctrYes 0.000000e+00
## Q120194.fctrTry first 0.000000e+00
## Q120472.fctrArt 0.000000e+00
## Q120650.fctrNo 0.000000e+00
## Q120978.fctrNo 0.000000e+00
## Q120978.fctrYes 0.000000e+00
## Q121011.fctrNo 0.000000e+00
## Q121011.fctrYes 0.000000e+00
## Q121699.fctrNo 0.000000e+00
## Q121700.fctrNo 0.000000e+00
## Q121700.fctrYes 0.000000e+00
## Q122120.fctrNo 0.000000e+00
## Q122769.fctrNo 0.000000e+00
## Q122769.fctrYes 0.000000e+00
## Q122770.fctrNo 0.000000e+00
## Q122770.fctrYes 0.000000e+00
## Q122771.fctrPc 0.000000e+00
## Q123464.fctrNo 0.000000e+00
## Q123464.fctrYes 0.000000e+00
## Q123621.fctrNo 0.000000e+00
## Q123621.fctrYes 0.000000e+00
## Q124122.fctrNo 0.000000e+00
## Q124122.fctrYes 0.000000e+00
## Q124742.fctrYes 0.000000e+00
## Q96024.fctrNo 0.000000e+00
## Q96024.fctrYes 0.000000e+00
## Q98059.fctrOnly-child 0.000000e+00
## Q98059.fctrYes 0.000000e+00
## Q98078.fctrNo 0.000000e+00
## Q98078.fctrYes 0.000000e+00
## Q98578.fctrNo 0.000000e+00
## Q98578.fctrYes 0.000000e+00
## Q98869.fctrYes 0.000000e+00
## Q99581.fctrNo 0.000000e+00
## Q99581.fctrYes 0.000000e+00
## Q99716.fctrNo 0.000000e+00
## Q99716.fctrYes 0.000000e+00
## Q99982.fctrCheck! 0.000000e+00
## Q99982.fctrNope 0.000000e+00
## YOB.Age.fctr.C 0.000000e+00
## YOB.Age.fctr.Q 0.000000e+00
## YOB.Age.fctr^4 0.000000e+00
## YOB.Age.fctr^5 0.000000e+00
## YOB.Age.fctr^6 0.000000e+00if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 107
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 1309 D 0.2170436
## 2 2641 D 0.2162383
## 3 1311 D 0.2067811
## 4 1393 D NA
## 5 3006 D 0.2212405
## 6 4956 D 0.2302791
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 R TRUE
## 2 R TRUE
## 3 R TRUE
## 4 <NA> NA
## 5 R TRUE
## 6 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.7829564 FALSE
## 2 0.7837617 FALSE
## 3 0.7932189 FALSE
## 4 NA NA
## 5 0.7787595 FALSE
## 6 0.7697209 FALSE
## Party.fctr.Final..rcv.glmnet.prob Party.fctr.Final..rcv.glmnet
## 1 0.2061977 R
## 2 0.2256276 R
## 3 0.2257340 R
## 4 0.2264941 R
## 5 0.2283662 R
## 6 0.2306508 R
## Party.fctr.Final..rcv.glmnet.err Party.fctr.Final..rcv.glmnet.err.abs
## 1 TRUE 0.7938023
## 2 TRUE 0.7743724
## 3 TRUE 0.7742660
## 4 TRUE 0.7735059
## 5 TRUE 0.7716338
## 6 TRUE 0.7693492
## Party.fctr.Final..rcv.glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final..rcv.glmnet.accurate Party.fctr.Final..rcv.glmnet.error
## 1 FALSE -0.4438023
## 2 FALSE -0.4243724
## 3 FALSE -0.4242660
## 4 FALSE -0.4235059
## 5 FALSE -0.4216338
## 6 FALSE -0.4193492
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 776 25 D 0.4496762
## 1017 695 D 0.5175311
## 1386 3812 D 0.5406492
## 1650 1729 D 0.6298912
## 1912 4924 D 0.5540143
## 2203 2226 R 0.7798730
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 776 R TRUE
## 1017 R TRUE
## 1386 R TRUE
## 1650 R TRUE
## 1912 R TRUE
## 2203 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 776 0.5503238
## 1017 0.4824689
## 1386 0.4593508
## 1650 0.3701088
## 1912 0.4459857
## 2203 0.7798730
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 776 FALSE 0.4920116
## 1017 FALSE 0.5154723
## 1386 FALSE 0.5440174
## 1650 FALSE 0.5686217
## 1912 FALSE 0.6015241
## 2203 FALSE 0.7291472
## Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 776 R TRUE
## 1017 R TRUE
## 1386 R TRUE
## 1650 R TRUE
## 1912 R TRUE
## 2203 D TRUE
## Party.fctr.Final..rcv.glmnet.err.abs
## 776 0.5079884
## 1017 0.4845277
## 1386 0.4559826
## 1650 0.4313783
## 1912 0.3984759
## 2203 0.7291472
## Party.fctr.Final..rcv.glmnet.is.acc
## 776 FALSE
## 1017 FALSE
## 1386 FALSE
## 1650 FALSE
## 1912 FALSE
## 2203 FALSE
## Party.fctr.Final..rcv.glmnet.accurate
## 776 FALSE
## 1017 FALSE
## 1386 FALSE
## 1650 FALSE
## 1912 FALSE
## 2203 FALSE
## Party.fctr.Final..rcv.glmnet.error
## 776 -0.15798844
## 1017 -0.13452765
## 1386 -0.10598258
## 1650 -0.08137834
## 1912 -0.04847587
## 2203 0.07914719
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 2330 1307 R NA
## 2331 2749 R 0.8687514
## 2332 1236 R 0.8691283
## 2333 1515 R 0.8899312
## 2334 3895 R 0.8914540
## 2335 626 R 0.9082257
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 2330 <NA> NA
## 2331 D TRUE
## 2332 D TRUE
## 2333 D TRUE
## 2334 D TRUE
## 2335 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 2330 NA
## 2331 0.8687514
## 2332 0.8691283
## 2333 0.8899312
## 2334 0.8914540
## 2335 0.9082257
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 2330 NA 0.8776340
## 2331 FALSE 0.8803600
## 2332 FALSE 0.8807433
## 2333 FALSE 0.8830267
## 2334 FALSE 0.8964361
## 2335 FALSE 0.8990037
## Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 2330 D TRUE
## 2331 D TRUE
## 2332 D TRUE
## 2333 D TRUE
## 2334 D TRUE
## 2335 D TRUE
## Party.fctr.Final..rcv.glmnet.err.abs
## 2330 0.8776340
## 2331 0.8803600
## 2332 0.8807433
## 2333 0.8830267
## 2334 0.8964361
## 2335 0.8990037
## Party.fctr.Final..rcv.glmnet.is.acc
## 2330 FALSE
## 2331 FALSE
## 2332 FALSE
## 2333 FALSE
## 2334 FALSE
## 2335 FALSE
## Party.fctr.Final..rcv.glmnet.accurate
## 2330 FALSE
## 2331 FALSE
## 2332 FALSE
## 2333 FALSE
## 2334 FALSE
## 2335 FALSE
## Party.fctr.Final..rcv.glmnet.error
## 2330 0.2276340
## 2331 0.2303600
## 2332 0.2307433
## 2333 0.2330267
## 2334 0.2464361
## 2335 0.2490037
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "Party.fctr.Final..rcv.glmnet.prob"
## [2] "Party.fctr.Final..rcv.glmnet"
## [3] "Party.fctr.Final..rcv.glmnet.err"
## [4] "Party.fctr.Final..rcv.glmnet.err.abs"
## [5] "Party.fctr.Final..rcv.glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.training.all.prediction
## 2.0000 5 2 0 0 1## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: model.final
## 3.0000 4 2 0 1 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 9 fit.data.training 5 1 1 318.738 328.479
## 10 predict.data.new 6 0 0 328.479 NA
## elapsed
## 9 9.741
## 10 NAStep 6.0: predict data new
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.65
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.65## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 107## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## NULL## Loading required package: tidyr##
## Attaching package: 'tidyr'## The following object is masked from 'package:Matrix':
##
## expand## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"## [1] 0.65## [1] "glbMdlSelId: All.X##rcv#glmnet"## [1] "glbMdlFinId: Final##rcv#glmnet"## [1] "Cross Validation issues:"
## MFO###myMFO_classfr Random###myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1###glmnet
## 0## max.Accuracy.OOB max.AUCROCR.OOB
## Low.cor.X##rcv#glmnet 0.5812500 0.3157430
## All.X##rcv#glmnet 0.5812500 0.3157430
## Interact.High.cor.Y##rcv#glmnet 0.5732143 0.3571392
## All.X##rcv#glm 0.5696429 0.3371452
## Max.cor.Y##rcv#rpart 0.5633929 0.3774772
## Max.cor.Y.rcv.1X1###glmnet 0.5633929 0.3658672
## Random###myrandom_classfr 0.4696429 0.5191202
## MFO###myMFO_classfr 0.4696429 0.5000000
## Final##rcv#glmnet NA NA
## max.AUCpROC.OOB max.Accuracy.fit
## Low.cor.X##rcv#glmnet 0.6261026 0.6254518
## All.X##rcv#glmnet 0.6261026 0.6254518
## Interact.High.cor.Y##rcv#glmnet 0.6031353 0.6058167
## All.X##rcv#glm 0.5989777 0.6049923
## Max.cor.Y##rcv#rpart 0.5896897 0.6000450
## Max.cor.Y.rcv.1X1###glmnet 0.5896897 0.5721673
## Random###myrandom_classfr 0.5235690 0.4700989
## MFO###myMFO_classfr 0.5000000 0.4700989
## Final##rcv#glmnet NA 0.6343978
## opt.prob.threshold.fit
## Low.cor.X##rcv#glmnet 0.60
## All.X##rcv#glmnet 0.60
## Interact.High.cor.Y##rcv#glmnet 0.65
## All.X##rcv#glm 0.65
## Max.cor.Y##rcv#rpart 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.60
## Random###myrandom_classfr 0.55
## MFO###myMFO_classfr 0.50
## Final##rcv#glmnet 0.60
## opt.prob.threshold.OOB
## Low.cor.X##rcv#glmnet 0.65
## All.X##rcv#glmnet 0.65
## Interact.High.cor.Y##rcv#glmnet 0.65
## All.X##rcv#glm 0.75
## Max.cor.Y##rcv#rpart 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.60
## Random###myrandom_classfr 0.55
## MFO###myMFO_classfr 0.50
## Final##rcv#glmnet NA## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference R D
## R 476 50
## D 419 175## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## PKy 24.55182 4.44135 29.00365 NA
## PKn 54.28673 14.22010 70.27487 NA
## N 169.17762 37.99992 208.01690 NA
## SKn 855.61896 233.08138 1093.06362 NA
## MKn 227.85830 61.65902 289.98913 NA
## SKy 61.84682 23.64664 86.84782 NA
## MKy 572.19199 130.77361 706.07068 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## PKy 0.01169065 0.008035714 0.007183908 52 3 7
## PKn 0.03372302 0.026785714 0.026580460 150 16 21
## N 0.08250899 0.074107143 0.073275862 367 11 91
## SKn 0.43165468 0.456250000 0.458333333 1920 120 518
## MKn 0.11600719 0.121428571 0.121408046 516 28 141
## SKy 0.03304856 0.047321429 0.046695402 147 11 54
## MKy 0.29136691 0.266071429 0.266522989 1296 49 322
## .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## PKy 9 35 26 10 52 10 61 0.4934833
## PKn 30 131 49 37 150 37 180 0.4740034
## N 83 230 220 102 367 102 450 0.4578304
## SKn 511 1340 1091 638 1920 638 2431 0.4561280
## MKn 136 344 308 169 516 169 652 0.4533752
## SKy 53 119 81 65 147 65 200 0.4461630
## MKy 298 752 842 371 1296 371 1594 0.4388376
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## PKy 0.4721503 NA 0.4754696
## PKn 0.3619116 NA 0.3904159
## N 0.4609745 NA 0.4622598
## SKn 0.4456349 NA 0.4496354
## MKn 0.4415859 NA 0.4447686
## SKy 0.4207267 NA 0.4342391
## MKy 0.4415062 NA 0.4429553
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 1965.532248 505.822031 2483.266662 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 4448.000000
## .n.New.D .n.New.R .n.OOB .n.Trn.D
## 238.000000 1154.000000 1120.000000 2951.000000
## .n.Trn.R .n.Tst .n.fit .n.new
## 2617.000000 1392.000000 4448.000000 1392.000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 5568.000000 3.219821 3.044490 NA
## err.abs.trn.mean
## 3.099744## [1] "Features Importance for selected models:"## All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Q109244.fctrYes 100.00000 100.0000000
## Hhold.fctrPKn 67.44709 41.1135979
## Q109244.fctrNo 48.99892 38.5639836
## Q115611.fctrYes 42.94308 35.9448112
## Q98869.fctrNo 34.81660 20.5482906
## Q98197.fctrNo 24.12262 30.9612021
## Q116881.fctrRight 18.54237 18.0112480
## Hhold.fctrMKy 18.29301 9.1538254
## Q99480.fctrNo 17.61676 4.0427224
## Income.fctr.C 16.96770 7.8080557
## YOB.Age.fctr.L 16.78320 7.0997165
## Q115611.fctrNo 15.93462 14.6199445
## Q118232.fctrId 15.69161 10.8720293
## Q120379.fctrYes 15.41041 9.7198751
## Q122771.fctrPt 15.02455 8.5254872
## Q119851.fctrNo 14.85551 11.0687511
## Edn.fctr^4 14.60100 0.0000000
## Q101163.fctrDad 14.59198 10.2622333
## Hhold.fctrSKy 14.46184 0.0000000
## Q113181.fctrYes 13.34329 15.5145602
## Q111220.fctrYes 13.25530 0.1522855
## Q100689.fctrYes 12.51301 8.7005370
## Q110740.fctrPC 12.43709 7.7413099
## Q113181.fctrNo 12.37742 11.4636192
## Q106997.fctrYy 11.33992 0.0000000
## Income.fctr.Q 11.26401 6.7052356
## Q115390.fctrNo 11.05396 1.0445476
## Q98197.fctrYes 10.98513 0.2579574
## Q115899.fctrCs 10.71373 5.4716495
## Gender.fctrM 10.43808 11.8191970
## Q116881.fctrHappy 10.21148 2.0532717
## Q101163.fctrMom 10.10753 6.0972450## [1] "glbObsNew prediction stats:"##
## R D
## 1154 238## label step_major step_minor label_minor bgn end
## 10 predict.data.new 6 0 0 328.479 343.606
## 11 display.session.info 7 0 0 343.606 NA
## elapsed
## 10 15.127
## 11 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 2 partition.data.training 2 0 0 9.292
## 4 fit.models 4 0 0 124.554
## 5 fit.models 4 1 1 198.236
## 8 fit.data.training 5 0 0 281.996
## 10 predict.data.new 6 0 0 328.479
## 6 fit.models 4 2 2 264.676
## 9 fit.data.training 5 1 1 318.738
## 3 select.features 3 0 0 118.368
## 7 fit.models 4 3 3 277.359
## 1 cluster.data 1 0 0 8.050
## end elapsed duration
## 2 118.368 109.076 109.076
## 4 198.235 73.681 73.681
## 5 264.675 66.439 66.439
## 8 318.737 36.741 36.741
## 10 343.606 15.127 15.127
## 6 277.359 12.683 12.683
## 9 328.479 9.741 9.741
## 3 124.554 6.186 6.186
## 7 281.996 4.637 4.637
## 1 9.292 1.242 1.242
## [1] "Total Elapsed Time: 343.606 secs"